Renewable energy sources play an increasingly important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases.
Out of all the renewable energy alternatives, wind energy is one of the most developed technologies worldwide. The U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices.
Predictive maintenance uses sensor information and analysis methods to measure and predict degradation and future component capability. The idea behind predictive maintenance is that failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower.
The sensors fitted across different machines involved in the process of energy generation collect data related to various environmental factors (temperature, humidity, wind speed, etc.) and additional features related to various parts of the wind turbine (gearbox, tower, blades, break, etc.).
“ReneWind” is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. They have shared a ciphered version of the data, as the data collected through sensors is confidential (the type of data collected varies with companies). Data has 40 predictors, 40000 observations in the training set and 10000 in the test set.
The objective is to build various classification models, tune them and find the best one that will help identify failures so that the generator could be repaired before failing/breaking and the overall maintenance cost of the generators can be brought down.
“1” in the target variables should be considered as “failure” and “0” will represent “No failure”.
The nature of predictions made by the classification model will translate as follows:
So, the maintenance cost associated with the model would be:
Maintenance cost = TP*(Repair cost) + FN*(Replacement cost) + FP*(Inspection cost)
where,
Replacement cost = $40,000Repair cost = $15,000Inspection cost = $5,000Here the objective is to reduce the maintenance cost so, we want a metric that could reduce the maintenance cost.
Actual failures*(Repair cost) = (TP + FN)*(Repair cost)TP*(Repair cost) + FN*(Replacement cost) + FP*(Inspection cost)So, we will try to maximize the ratio of minimum possible maintenance cost and the maintenance cost associated with the model.
The value of this ratio will lie between 0 and 1, the ratio will be 1 only when the maintenance cost associated with the model will be equal to the minimum possible maintenance cost.
import pandas as pd
import numpy as np
# importing ploting libraries
import matplotlib.pyplot as plt
#importing seaborn for statistical plots
import seaborn as sns
# To enable plotting graphs in Jupyter notebook
%matplotlib inline
#Let us break the X and y dataframes into training set and validation set. For this we will use
#Sklearn package's data splitting function which is based on random function
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
# To be used for missing value imputation
from sklearn.impute import SimpleImputer
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# To build model for prediction
from sklearn.linear_model import LogisticRegression
# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.linear_model import LogisticRegression
# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn import metrics
# To tune different models
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
# To get diferent metric scores
from sklearn.metrics import (
f1_score,
accuracy_score,
recall_score,
precision_score,
confusion_matrix,
roc_auc_score,
plot_confusion_matrix,
precision_recall_curve,
roc_curve,
)
# Libraries to import decision tree classifier and different ensemble classifiers
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, StackingClassifier
from xgboost import XGBClassifier
# To be used for creating pipelines
from sklearn.pipeline import Pipeline
import warnings
warnings.filterwarnings('ignore')
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import RandomOverSampler
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
# loading the train dataset
data = pd.read_csv("Train.csv")
# Make a copy of train dataset
df = data.copy()
# loading the test dataset
data_test = pd.read_csv("Test.csv")
# Make a copy of test dataset
df_test = data_test.copy()
np.random.seed(1)
data.sample(200)
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | V29 | V30 | V31 | V32 | V33 | V34 | V35 | V36 | V37 | V38 | V39 | V40 | Target | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3841 | -1.761261 | 1.725575 | 3.115833 | -0.722076 | 1.636716 | -0.665690 | -1.947430 | 0.340637 | -0.434492 | -1.861200 | -0.748612 | 1.483686 | 3.233458 | -1.749122 | -5.027887 | -1.380684 | -1.399922 | 2.132650 | -1.505987 | -2.451394 | -4.512434 | 0.036412 | 0.488204 | 2.010276 | -2.194374 | 5.600887 | -4.419503 | -3.459057 | 1.848695 | 3.183373 | 3.131519 | 4.559034 | 5.339557 | -4.860047 | 3.971955 | 1.084243 | -0.286629 | -2.123846 | 0.088027 | 0.467075 | 0 |
| 12898 | -0.319513 | -3.223125 | 6.945362 | -6.073991 | 0.560963 | -1.151006 | -1.368454 | -0.203942 | -3.529692 | 5.112241 | -3.728806 | 2.433346 | 3.220423 | 1.316607 | -1.652448 | -0.902062 | -6.406294 | 2.609491 | -0.583582 | -3.180174 | -3.682714 | -0.107914 | -3.111936 | 1.132707 | 1.378427 | 2.399556 | 5.679397 | -3.810228 | -4.106580 | -3.610371 | 9.925717 | 2.086696 | 0.300373 | -0.581224 | 2.849599 | 11.783096 | -1.036491 | -2.656733 | 4.048503 | -0.043561 | 0 |
| 15032 | 3.660333 | -0.500171 | 1.673259 | -0.759881 | -3.446873 | -0.177149 | -0.150797 | -0.962328 | 0.798321 | -0.378106 | 2.456049 | 1.879260 | 2.194074 | -1.375423 | -1.303997 | -3.645691 | -1.916947 | -0.564766 | 3.382290 | -0.034927 | -5.565015 | 2.032844 | -0.867979 | -1.966172 | 0.320279 | -0.502116 | 2.028311 | -0.768731 | 1.730565 | 3.023456 | -2.705545 | -3.397233 | 0.711632 | 0.350068 | 5.073397 | -2.271627 | -0.376924 | -0.115856 | -0.630295 | -1.382311 | 0 |
| 36781 | -2.031378 | -7.075963 | 1.609581 | -2.808285 | -2.626699 | -3.161704 | -2.401735 | 2.357427 | -1.254624 | 2.679572 | -4.866034 | 5.473158 | 5.339906 | 2.596990 | -5.179196 | -2.976010 | -0.568500 | 3.978816 | 1.724852 | 6.053674 | -5.327246 | 3.560616 | 3.365471 | -0.391724 | 1.284162 | -5.200010 | 2.720958 | -0.473470 | -4.738653 | 0.069819 | -1.207865 | 0.449110 | -5.094617 | 2.979341 | 2.545381 | 7.661457 | 2.721677 | -5.888977 | 3.233609 | -2.895987 | 0 |
| 9201 | -1.167479 | 4.636223 | -1.011644 | 0.316850 | 3.275565 | -0.310062 | 1.388811 | 4.651706 | -4.848882 | -2.074384 | 3.231886 | 5.032511 | -2.634830 | -2.825284 | 1.290487 | 6.432091 | 0.251476 | -1.082105 | 0.626093 | -7.337963 | 0.912257 | -0.578727 | 5.034060 | 8.326799 | -4.863529 | 5.333237 | -8.192814 | -1.593566 | 5.291905 | 6.553607 | 5.233752 | 10.877396 | 8.250367 | -5.159849 | 4.674169 | -3.516928 | -1.730339 | 1.969370 | -3.425079 | 2.554127 | 0 |
| 21288 | -3.187048 | 3.810940 | 0.850021 | 1.188452 | 3.331867 | -1.399935 | -1.550283 | 0.635168 | -0.389022 | -2.848663 | -3.003486 | 3.366907 | 2.550711 | -2.365782 | -6.294215 | -2.694268 | 1.701079 | 3.318560 | -0.941472 | -2.522348 | -4.691335 | 1.915986 | 4.572873 | 6.014727 | -3.252703 | 6.471669 | -7.581418 | -2.034078 | -0.284077 | 2.482489 | 0.878916 | 7.499800 | 3.778780 | -5.057147 | 2.754992 | -0.312605 | 0.304002 | 0.109490 | -0.080787 | -0.263837 | 0 |
| 37321 | 5.669580 | 1.844251 | 7.218002 | 1.555360 | -3.158366 | -2.388045 | -0.937380 | -2.299472 | 3.368328 | -0.475084 | -1.593526 | -0.763408 | 4.474542 | -1.536328 | -2.689027 | -7.051011 | -1.965468 | -2.414158 | 3.769097 | 0.893762 | -9.227253 | 1.080952 | -4.955377 | -3.369827 | 2.379708 | 4.442623 | 3.109184 | -2.405830 | -1.814943 | -1.632428 | 0.736474 | -4.127790 | -1.698119 | -0.144663 | 6.158802 | -0.501844 | -2.075539 | -3.096800 | 0.029782 | -2.895171 | 0 |
| 8600 | -2.459670 | -2.393870 | 3.225757 | 0.063384 | -0.580143 | -2.797114 | -0.634555 | 2.118902 | -2.976534 | 2.924087 | -3.354634 | 4.535033 | 0.790923 | 0.388404 | -0.780539 | 0.137287 | -0.147010 | 0.193798 | 4.075666 | 1.939926 | -1.893998 | 0.262768 | -0.026678 | 3.035784 | 0.127010 | -0.972351 | 0.181684 | 0.249843 | -2.562915 | -0.478893 | 2.429896 | 2.392210 | -2.040141 | 3.390597 | 2.891005 | 5.108812 | 0.085175 | -1.349147 | 2.056523 | -2.479685 | 0 |
| 33089 | 1.001489 | 0.983718 | -2.561203 | 3.374567 | 2.495414 | -0.720727 | 0.067609 | 3.662200 | 0.327424 | -1.763364 | -3.399646 | -4.088555 | -5.765949 | 1.293401 | 4.038515 | 3.405233 | 6.860836 | -0.479386 | -3.237459 | 2.401008 | 4.419077 | -0.393963 | 2.743099 | 4.115304 | 1.274670 | -1.566217 | -4.673000 | 2.077497 | -0.692994 | -1.068473 | 2.815314 | 5.783949 | -1.469405 | -2.904733 | -2.098224 | -2.400717 | -0.437158 | -3.385150 | -3.896752 | 3.312009 | 1 |
| 39511 | -3.648807 | -1.589335 | -0.575212 | 0.649347 | 1.329551 | -2.730112 | -2.246243 | 2.806568 | -0.600226 | -1.071473 | -5.598787 | 4.039250 | 2.624120 | 0.526896 | -5.738545 | -2.256579 | 3.684117 | 4.407944 | -0.596813 | 3.524609 | -3.745387 | 3.215177 | 6.282294 | 4.594613 | -1.079856 | -0.412728 | -5.065619 | -0.157468 | -3.058230 | 1.398945 | -1.026855 | 6.162144 | -1.610211 | -1.636248 | 1.450378 | 2.772455 | 2.196238 | -3.787583 | 0.694094 | -1.008109 | 0 |
| 6339 | -3.551815 | 3.627793 | 2.535088 | 0.661531 | 2.249379 | 0.149978 | -0.695914 | -1.542848 | -1.292379 | -0.389810 | 1.463685 | 1.747273 | 2.459637 | -2.623354 | -4.086552 | -0.446178 | -3.474802 | 0.032576 | 1.106289 | -5.303431 | -2.428616 | -1.387931 | -0.973323 | 3.125872 | -3.922586 | 7.289220 | -4.123846 | -2.536259 | 2.161735 | 2.661455 | 2.409424 | 4.749548 | 6.839884 | -2.351271 | 3.208397 | -0.284048 | -1.575773 | 3.078558 | 1.032238 | -1.870083 | 0 |
| 18278 | 2.550787 | 2.811435 | -2.203186 | 3.597875 | -2.657331 | 1.650337 | 0.140312 | -4.771109 | 4.283019 | -2.588010 | 3.593302 | -1.913560 | 0.585375 | -2.719250 | -2.232610 | -6.939040 | 0.895071 | -0.946896 | 3.611871 | 0.500683 | -3.987344 | 2.281473 | -0.782765 | -1.960175 | -0.776697 | 0.131384 | -0.713410 | 1.862286 | 2.230179 | 2.787261 | -9.699605 | -5.335874 | 0.920126 | 0.892907 | 1.929155 | -8.995227 | -0.358596 | 5.075685 | -1.468510 | -3.104654 | 0 |
| 35884 | 4.596979 | 3.894733 | 8.591565 | 1.622412 | -0.025474 | -2.145720 | -1.156491 | -4.832266 | 4.655049 | 0.150269 | -6.321445 | -3.868884 | 3.716084 | -1.049513 | -2.899521 | -9.287436 | -0.946999 | -0.763548 | 0.582381 | 0.080861 | -7.786255 | 0.418975 | -6.705846 | -2.486450 | 3.934307 | 7.552288 | 4.090375 | -2.473378 | -6.413258 | -8.268339 | 3.564723 | -4.435940 | -4.389099 | -1.045104 | 1.414291 | 3.235155 | -2.236143 | -1.829380 | 1.977927 | -2.026602 | 0 |
| 21864 | -1.565708 | -3.027466 | 0.532782 | -0.981795 | -0.674058 | -3.942254 | -1.223699 | 5.222253 | -0.966883 | -1.303492 | -4.474402 | 6.410118 | 4.150539 | 1.380906 | -3.843209 | 0.519094 | 2.998041 | 2.450724 | -0.193935 | 5.184919 | -3.528687 | 2.527152 | 5.864790 | 0.449380 | 0.369428 | -2.040906 | -2.213614 | -0.992514 | -2.510545 | 1.094713 | -0.571076 | 2.411185 | -4.399495 | 0.037628 | 1.299077 | 4.035997 | 3.040682 | -7.075108 | 0.778022 | 0.083708 | 0 |
| 2349 | 0.840235 | -0.708632 | 3.308609 | 1.225660 | -2.454219 | -1.112553 | -1.578783 | -2.450879 | 1.070890 | 1.429553 | -0.427080 | 0.696029 | 3.592329 | -0.991482 | -4.292428 | -6.009771 | -2.691477 | -0.046976 | 4.574234 | 0.527891 | -7.008151 | 1.886849 | -1.870372 | 0.294148 | -0.363253 | 1.259020 | 1.186829 | -0.704907 | -1.175926 | 1.441918 | -1.699301 | -0.038734 | 0.940844 | 1.262111 | 5.873557 | -0.187664 | -1.320520 | 0.174840 | 0.987290 | -4.787977 | 0 |
| 25013 | 5.473713 | 1.951747 | 6.461266 | -3.854035 | -0.655628 | -0.855989 | 0.993979 | -1.962615 | -0.041821 | 1.608538 | -1.915791 | 1.577255 | 2.169535 | -0.877597 | 0.930473 | -3.609019 | -3.868829 | -0.425611 | 0.691753 | -3.933995 | -5.025604 | 0.755805 | -3.921181 | -1.768228 | 2.953528 | 4.586922 | 6.588051 | -2.958756 | -3.428691 | -5.364961 | 5.632791 | -4.442961 | -2.647415 | -0.741950 | 1.774159 | 5.111057 | -1.485694 | -0.018834 | 1.848675 | 1.086623 | 0 |
| 4325 | -6.463405 | -1.880938 | -4.762638 | 0.199117 | 3.449166 | -0.215322 | -2.563050 | 1.229620 | -2.340841 | 0.862433 | -3.569687 | 2.610820 | 1.028105 | 0.835793 | -6.883875 | -1.589325 | 1.316685 | 6.418178 | -1.507703 | -0.865432 | -1.614967 | 4.134527 | 9.248107 | 9.807328 | -4.330478 | -0.633986 | -6.814751 | 0.658556 | -2.590367 | 3.744640 | -0.797244 | 13.146015 | 3.642709 | -3.338491 | 1.528567 | 1.660885 | 1.008317 | 0.880424 | 0.439432 | -2.323885 | 0 |
| 18444 | -3.922592 | -0.716424 | -0.625177 | -0.759416 | 0.269264 | 1.448407 | -1.419675 | -3.490800 | 1.207244 | -0.854969 | -1.431802 | 0.437404 | 0.505923 | -1.243799 | -3.435204 | -4.844258 | 1.582460 | 4.333682 | -0.704234 | 2.289792 | -0.652455 | 0.757898 | -1.011178 | -1.161557 | -0.020075 | -1.187037 | -0.838326 | 0.997918 | 0.077379 | -0.155061 | -5.480609 | -5.294758 | -0.671548 | 1.168507 | -2.807358 | 1.195501 | 2.728818 | 3.184945 | 2.753287 | -0.060097 | 0 |
| 38567 | -2.060733 | -1.003138 | 2.378707 | 8.653939 | -3.660531 | -2.916685 | -3.838112 | -2.161073 | 5.590147 | -2.069150 | -2.487727 | -3.092337 | 4.367625 | -0.947978 | -7.509704 | -8.311955 | 3.520546 | -1.394514 | 5.670233 | 9.419138 | -7.409680 | 0.874912 | -2.684386 | -2.221372 | -0.253464 | -0.547036 | -4.696498 | 1.550645 | -0.187396 | 3.413894 | -8.847888 | -1.224028 | -0.913793 | 3.074301 | 5.541886 | -5.769182 | -0.041936 | -3.776050 | -0.406687 | -7.785024 | 0 |
| 35807 | -3.580043 | -3.064686 | 7.141889 | -0.014793 | -0.266093 | -2.890595 | -2.850570 | -0.466764 | -0.638960 | 3.365612 | -5.346431 | 0.462645 | 4.301976 | 1.083240 | -4.321141 | -2.622288 | -2.668333 | 0.864587 | 1.747356 | 3.025243 | -3.999083 | -1.456625 | -4.634657 | -0.444269 | 1.019802 | 1.963322 | 1.585671 | -2.064614 | -3.924173 | -2.772637 | 4.765179 | 1.147408 | -1.184054 | 2.259933 | 2.736258 | 8.340308 | -0.436322 | -4.395049 | 3.983276 | -3.657410 | 0 |
| 21574 | 2.234900 | -1.157103 | 2.641155 | 1.808258 | -1.581913 | -4.262033 | -0.625656 | 3.917945 | 1.507766 | -1.092715 | -3.913440 | 1.612656 | 3.719144 | 1.679615 | -1.505035 | 0.095622 | 2.217786 | -1.806435 | 0.692923 | 5.126791 | -3.620675 | 0.864610 | 1.696946 | -2.038346 | 1.997897 | -0.092487 | 0.027894 | -1.075806 | -3.205509 | -1.516900 | 1.098522 | 0.973651 | -5.246278 | 0.460730 | 1.905400 | 1.471946 | 0.453076 | -8.082783 | -0.646265 | -1.126860 | 0 |
| 32747 | -4.725562 | -2.248215 | 1.219893 | -2.935253 | 3.501466 | -0.561866 | -0.935297 | 2.340625 | -3.417588 | 2.377341 | -3.901312 | 0.807640 | -0.440733 | 2.046685 | -0.033397 | 4.322583 | -0.735747 | 2.731379 | -3.974492 | -0.758029 | 3.711597 | -1.868496 | 0.674777 | 2.821120 | -0.183075 | 0.506056 | -0.587247 | -1.322181 | -2.365365 | -2.609281 | 7.313353 | 5.273594 | 0.328926 | -1.123492 | -2.873136 | 8.301693 | 0.707592 | -2.464322 | 2.344760 | 2.088599 | 0 |
| 6459 | -2.494747 | 0.509737 | 0.690148 | -1.923434 | 0.137447 | 0.075722 | -1.329587 | -1.447293 | 0.811800 | -1.970586 | 0.498927 | 3.276732 | 5.439281 | -1.384076 | -6.384316 | -3.119616 | -2.037485 | 3.047347 | -1.045088 | -0.813817 | -4.330428 | 1.225205 | 1.512275 | -1.684131 | -2.001328 | 3.005831 | -1.593295 | -2.486159 | 0.982435 | 2.341432 | -2.846907 | -1.174303 | 2.199412 | -1.832026 | 0.852631 | 0.913123 | 1.807781 | 0.058914 | 2.047611 | -0.708221 | 0 |
| 15625 | -0.763755 | 5.732489 | -1.142809 | 2.543378 | 2.062929 | -3.028471 | 2.911480 | 4.501955 | -2.723536 | -3.321590 | -1.956316 | 9.201125 | -2.490716 | -3.146032 | 1.893023 | 2.367804 | 6.482009 | -1.080398 | 3.793281 | -1.166446 | 0.125941 | 1.521532 | 6.353062 | 6.382135 | -1.633567 | 2.948316 | -6.493122 | 1.599143 | -0.127881 | 0.551903 | -1.657231 | 2.420604 | -3.085171 | 0.705331 | -0.125619 | -2.811951 | 1.342785 | 3.220445 | -1.266197 | 1.569771 | 0 |
| 33301 | -2.339788 | -0.076180 | 0.308177 | -2.621841 | 2.094128 | 1.334731 | -2.315580 | -0.683228 | -0.827968 | -1.269572 | -0.692024 | 0.262791 | 1.073477 | -1.175177 | -4.518297 | -2.160441 | -0.750432 | 5.134011 | -3.563724 | -2.536550 | -2.779728 | 1.278835 | 1.805907 | 3.082114 | -1.947695 | 2.413409 | -3.824757 | -2.273773 | 1.935879 | 3.518153 | 1.832991 | 4.272880 | 5.703544 | -5.597877 | 2.103349 | 1.632345 | 0.693053 | -0.404033 | 0.160932 | 2.013161 | 0 |
| 20958 | -3.222824 | -1.975180 | 3.360669 | 1.599395 | -2.172741 | -0.479823 | 0.013482 | -3.210058 | -2.942850 | 5.673646 | -0.776975 | 2.936614 | -2.249902 | -1.473941 | 1.041365 | -3.717479 | -1.817574 | -0.910715 | 9.335807 | 0.811739 | -1.146105 | -0.340371 | -5.288669 | 3.323430 | 0.006831 | -2.470987 | 2.920775 | 3.152961 | -1.684626 | -0.869656 | -1.781654 | -2.955383 | -1.151673 | 8.367510 | 3.213500 | 2.861327 | -1.182047 | 6.993016 | 3.567873 | -5.759299 | 0 |
| 4158 | 0.120862 | -0.667378 | -1.125594 | -3.734646 | 0.021899 | 1.091254 | 0.147303 | 1.770718 | -1.543286 | -2.234447 | 1.500537 | 3.222176 | -0.981953 | -1.061685 | 0.593982 | 1.451157 | 1.328523 | 2.896264 | -2.407867 | -0.515469 | 0.351025 | 0.745743 | 2.073998 | -0.512395 | -0.105752 | -1.562816 | -1.376730 | -0.811193 | 3.634068 | 3.227380 | -0.613576 | -2.245694 | 1.437741 | -2.434399 | -0.302820 | 0.404117 | 2.349080 | -0.508355 | -0.555266 | 4.604935 | 0 |
| 36250 | -3.068931 | 4.020179 | -1.414283 | 1.089811 | 1.399730 | 1.980675 | 1.410602 | -1.666463 | -2.189472 | -0.899008 | 4.392814 | 2.706882 | -2.770147 | -3.678268 | 0.835149 | 1.130911 | -0.470201 | -0.687779 | 2.968726 | -4.911742 | 1.958650 | -1.321512 | -0.461396 | 3.463826 | -3.659622 | 2.904381 | -4.093175 | 0.940650 | 4.810282 | 3.825329 | -2.336805 | 0.281260 | 5.573797 | 0.504742 | 0.669392 | -4.269208 | -0.533053 | 8.181273 | -0.119736 | -0.162695 | 0 |
| 39306 | 5.253584 | 3.457338 | 2.675185 | 1.078791 | -1.899951 | 0.153047 | 0.159860 | -3.311630 | 1.398147 | -0.426058 | 1.262450 | 0.449341 | 0.537079 | -3.179537 | -1.408544 | -7.120349 | -1.487109 | -0.687421 | 4.587871 | -3.563034 | -7.675911 | 3.079183 | -1.325215 | 2.200203 | -0.101635 | 3.584976 | 0.719199 | -0.522720 | 0.340779 | 1.591804 | -1.467295 | -0.876200 | 2.091505 | -1.520999 | 6.630761 | -4.306079 | -2.472735 | 3.595412 | -1.446394 | -2.142120 | 0 |
| 34064 | -4.958353 | -2.110731 | -3.402645 | 3.056260 | 2.526045 | -2.703365 | -1.429251 | 4.932550 | -1.665018 | 0.806639 | -3.360878 | 0.932650 | 1.500957 | 2.971420 | -3.326486 | 4.391343 | 1.835226 | 0.531274 | -1.106179 | 2.165046 | 1.652557 | 0.724257 | 7.398975 | 5.977177 | -2.938016 | -0.829546 | -5.811433 | 0.467218 | -2.966283 | 1.711641 | 2.099312 | 13.296631 | 0.575342 | -1.048665 | 0.508342 | 1.121608 | -0.091407 | -4.662793 | -0.896117 | -2.841350 | 0 |
| 26790 | -4.034322 | -5.587398 | 1.194270 | -3.057782 | -0.734225 | 0.977874 | -3.228967 | 0.401985 | -2.367992 | 2.821711 | 2.887213 | -0.702576 | 4.055465 | 1.549651 | -4.869723 | 1.901247 | -6.895516 | 2.101894 | -1.190682 | -1.207422 | -1.751770 | -1.081032 | -0.863360 | -0.100078 | -3.003411 | -0.876283 | -0.287975 | -3.075196 | 3.210186 | 6.212919 | 3.669055 | 6.265691 | 8.219055 | -1.957455 | 5.206397 | 3.752150 | -0.750523 | -3.614659 | 1.147416 | -2.166579 | 0 |
| 20664 | 6.303344 | 4.519608 | 6.487612 | -2.017057 | -1.325153 | 0.487633 | 1.208371 | -5.810485 | 0.415405 | 1.504238 | -1.307161 | 1.883920 | -0.747074 | -4.104476 | 0.895848 | -9.595160 | -2.399612 | 0.467805 | 5.002067 | -5.470026 | -7.840741 | 2.727570 | -5.342668 | 1.617674 | 2.526455 | 5.091980 | 5.491654 | -0.770311 | -2.522880 | -4.135832 | 1.291051 | -6.930345 | -1.409791 | 0.275222 | 4.219153 | 1.161533 | -2.222784 | 6.875730 | 1.553857 | -0.406248 | 0 |
| 20150 | 2.460320 | 5.616156 | 4.759398 | 3.300752 | -0.585693 | 0.575757 | -0.041043 | -7.716506 | 3.708751 | -0.127053 | -1.637960 | -2.432218 | 0.040552 | -4.008590 | -1.895715 | -10.716765 | -0.235779 | -0.678639 | 4.770799 | -2.003138 | -6.098695 | 0.941366 | -6.552417 | 0.180430 | 1.232052 | 5.865364 | 1.654950 | 0.675040 | -2.235893 | -3.982709 | -3.358469 | -6.608694 | -0.881325 | 1.468744 | 1.843759 | -2.820394 | -2.105525 | 7.208601 | 1.552514 | -3.476688 | 0 |
| 39985 | -0.730383 | -8.463166 | 1.111823 | -8.918874 | -2.309441 | 2.902515 | -4.203021 | -3.019003 | -0.093653 | 2.472905 | 0.468690 | -1.062925 | 5.313560 | 2.043371 | -6.505954 | -5.233454 | -6.589395 | 8.245479 | -5.011889 | 0.658846 | -5.277002 | 2.836614 | -0.947907 | -4.253186 | 0.860204 | -4.562051 | 5.660413 | -3.458896 | 0.611588 | 3.127485 | 0.417374 | -2.711114 | 3.624536 | -3.264870 | 2.393250 | 7.795103 | 2.312051 | -3.950337 | 3.226444 | 1.138433 | 0 |
| 33160 | 4.034334 | -4.226442 | -3.640527 | -7.109022 | -1.186211 | 0.491667 | 1.822823 | 5.687068 | -1.396337 | -1.477452 | 2.196139 | 2.608271 | 0.318035 | 3.365692 | 4.484482 | 7.579414 | 0.113756 | 1.032824 | -6.583616 | 1.239309 | 4.310933 | 0.563323 | 4.753579 | -5.875200 | 2.550794 | -6.002123 | 4.538539 | -1.504074 | 1.382385 | 0.037851 | 1.922059 | -3.578728 | -3.881707 | -2.026844 | -4.471137 | 3.113900 | 3.245895 | -6.709455 | -1.309275 | 7.258371 | 1 |
| 33130 | 2.119447 | -2.307722 | 5.208642 | -3.827572 | -1.701251 | -0.014460 | -1.808990 | -1.233677 | -0.928097 | 2.095686 | 0.076144 | 0.441102 | 3.093811 | -0.294475 | -2.527438 | -3.225541 | -5.176680 | 1.657553 | 0.540341 | -2.054929 | -6.006471 | 0.831631 | -3.202310 | -0.767210 | 0.661016 | 1.505628 | 3.403402 | -3.262342 | 0.306956 | 1.392471 | 4.506983 | -0.000073 | 3.142020 | -1.993242 | 5.845560 | 4.365395 | -1.222885 | -2.451573 | 1.148242 | -0.405682 | 0 |
| 27327 | 2.001465 | 5.740983 | 10.654401 | 4.815918 | -0.465645 | -1.116768 | -2.336129 | -8.314246 | 2.973246 | 0.943020 | -5.986439 | -2.641915 | -0.804316 | -5.668633 | -3.729763 | -15.263104 | 0.944473 | 0.678128 | 7.539784 | -1.041442 | -11.155836 | 1.217613 | -10.038303 | 4.318149 | 2.040079 | 8.246908 | -0.579973 | 0.071510 | -3.119205 | -3.686181 | 0.391795 | -4.156189 | 0.623127 | 0.584500 | 7.424981 | -0.186681 | -3.473469 | 5.878457 | 1.913055 | -4.633420 | 0 |
| 5638 | -1.534489 | 3.787510 | 1.881309 | -0.006817 | 2.162919 | 0.240804 | 0.213320 | -1.563365 | -0.392481 | -1.903235 | -2.498041 | 2.159417 | -2.189028 | -3.083150 | -0.187904 | -2.934785 | 3.244472 | 2.357684 | 0.048708 | -1.330165 | -0.720197 | 0.046061 | -1.068476 | 2.558338 | 0.146927 | 3.414449 | -2.951535 | 0.242550 | 0.276611 | -1.410619 | -0.388310 | -2.689981 | -0.206283 | -0.981977 | -1.386299 | 0.636871 | 1.093949 | 3.917184 | 1.075795 | 2.351651 | 0 |
| 25843 | 3.010396 | 0.803118 | 7.098950 | -1.183318 | -0.622942 | -1.397743 | -1.482802 | -2.329223 | -0.713550 | 2.553778 | -3.460079 | 0.959994 | 1.672045 | -1.681166 | -2.557599 | -6.851561 | -3.063928 | 1.320302 | 3.380660 | -2.973135 | -8.488356 | 2.162360 | -3.250969 | 3.702127 | 1.106059 | 4.416719 | 2.223687 | -2.239366 | -2.994119 | -1.383587 | 5.308345 | 1.906866 | 1.259658 | -1.620502 | 7.198764 | 4.034118 | -2.598595 | 0.059734 | 1.137319 | -2.024605 | 0 |
| 11862 | -1.092607 | 8.109485 | 4.941355 | 2.990075 | 3.353221 | -1.230571 | -0.198908 | -0.731369 | -0.007310 | -3.965366 | -1.774463 | 2.107957 | -0.317216 | -5.112547 | -2.408188 | -2.578410 | 2.375784 | -0.216038 | 1.161500 | -4.349426 | -4.054044 | -1.149392 | -1.605463 | 4.730027 | -2.143236 | 10.425071 | -7.697807 | -2.213738 | 2.301517 | 1.105044 | 2.844153 | 2.778878 | 4.627117 | -4.454434 | 3.336092 | -2.503542 | -1.327995 | 2.597291 | -0.772915 | 1.191254 | 0 |
| 36997 | -4.150393 | -1.809235 | -1.488128 | -0.197908 | 1.564588 | 0.910811 | -2.158123 | 1.227422 | -1.901470 | -0.111964 | 0.535459 | -0.456229 | -0.689806 | -0.053294 | -2.671291 | 1.509173 | -0.026241 | 2.878479 | -1.747314 | -0.573402 | 0.505177 | -0.143205 | 2.096627 | 4.001927 | -2.813376 | -0.504636 | -5.168078 | -0.507422 | 3.013752 | 5.280892 | 1.337067 | 6.966570 | 6.192978 | -3.114945 | 2.394145 | 0.048979 | 0.156462 | -0.934166 | -0.795109 | 0.353409 | 0 |
| 17264 | -2.997211 | 1.244078 | 3.735073 | 1.416558 | 4.545031 | -3.917550 | -2.824292 | 5.561570 | -3.194602 | -0.533483 | -6.758816 | 2.282227 | 1.080296 | -0.006486 | -4.576211 | 1.685245 | 2.101132 | 2.599749 | -1.959354 | -1.711690 | -4.186596 | 0.863195 | 5.099658 | 10.331290 | -2.478798 | 6.228071 | -9.000229 | -3.195940 | -2.001328 | 2.312380 | 10.281256 | 16.720223 | 4.628075 | -7.171912 | 6.242135 | 4.250456 | -1.591142 | -6.824766 | -1.615442 | 0.523060 | 0 |
| 15887 | -5.119528 | -2.134942 | -3.867764 | -0.167196 | 2.401703 | 1.471377 | -0.913480 | 2.439170 | -5.082605 | 2.314940 | 2.038110 | 0.919725 | -3.814131 | 0.275792 | 0.108335 | 4.838327 | -0.857327 | 2.031887 | 0.189098 | -3.425093 | 3.531769 | -0.056936 | 4.204841 | 8.728807 | -4.434598 | -1.990582 | -5.561698 | 1.050231 | 3.165012 | 6.380168 | 3.006927 | 11.820389 | 7.887095 | -1.927630 | 3.014197 | -0.279033 | -1.088092 | 2.423303 | -1.445247 | -0.345605 | 0 |
| 1607 | -3.194211 | 3.656321 | 0.240743 | 0.580751 | 2.637754 | 0.869519 | -0.350041 | -1.105734 | -1.342316 | -1.573575 | 0.520183 | 1.885374 | -0.864115 | -3.016421 | -2.262501 | -0.960089 | 0.416448 | 1.821479 | 0.216387 | -3.953482 | -0.853223 | -0.188189 | 0.783702 | 4.568547 | -3.038835 | 4.643039 | -5.423311 | -0.667682 | 2.526702 | 2.755093 | 0.236321 | 3.345590 | 5.224805 | -2.701986 | 1.359061 | -1.516612 | -0.217445 | 4.262051 | 0.072057 | 0.518670 | 0 |
| 36021 | 2.985261 | -0.659371 | 0.418381 | -2.621510 | -0.280675 | -1.167341 | 0.902724 | 2.700924 | 0.220452 | -0.929860 | -1.089127 | 1.359233 | 1.545755 | 1.564770 | 1.453703 | 2.363280 | 0.290727 | -0.260281 | -2.888080 | 0.790177 | 0.230101 | 0.353778 | 1.693290 | -3.057707 | 2.059231 | -0.477783 | 2.633279 | -1.495676 | -1.537705 | -2.393007 | 2.351764 | -1.737449 | -3.777159 | -1.146986 | -1.927076 | 2.526691 | 1.071135 | -4.534372 | -0.245533 | 2.905994 | 0 |
| 197 | 4.321706 | 1.342939 | 6.074039 | -3.162261 | -0.975497 | -0.485409 | 0.393169 | -1.352196 | -0.668967 | 1.267787 | -0.279633 | 1.366989 | 1.555722 | -1.342179 | 0.656982 | -2.541723 | -3.883069 | -0.603350 | 1.281327 | -3.726941 | -4.746187 | 0.120364 | -4.117137 | -1.104561 | 1.766755 | 3.977211 | 4.434324 | -2.883538 | -0.743803 | -2.098933 | 5.241477 | -2.899408 | 0.298458 | -1.154131 | 3.634171 | 3.503677 | -1.674105 | -0.233532 | 0.978003 | 0.895583 | 0 |
| 31063 | 2.045746 | 1.646281 | -0.542425 | -3.902446 | -0.121636 | 3.617061 | 1.292684 | -4.213137 | 0.206874 | -0.115684 | 3.643017 | -0.567639 | -1.320304 | -1.800854 | 1.684966 | -2.130913 | -2.803191 | 1.461850 | -1.316657 | -4.653683 | 0.598754 | 0.160177 | -2.622383 | -2.326379 | 0.381206 | 0.790137 | 3.747752 | -0.425508 | 2.182431 | -0.651021 | -1.399496 | -6.651369 | 1.829979 | -0.845275 | -2.056598 | -0.601709 | 0.215999 | 5.923888 | 0.932563 | 2.596693 | 0 |
| 4498 | 1.555111 | -1.213178 | 5.966381 | 0.253198 | -3.993673 | -0.842711 | -1.970585 | -4.794925 | 1.052861 | 2.728360 | -0.273018 | 1.433432 | 4.292044 | -2.081505 | -5.015968 | -9.554788 | -4.540599 | 0.430071 | 7.085111 | 0.208838 | -9.960268 | 2.365868 | -4.944841 | -0.537269 | 0.347064 | 1.408458 | 3.656894 | -0.931716 | -1.270103 | 1.241827 | -2.356105 | -3.620784 | 0.986029 | 2.803930 | 8.001732 | 1.092050 | -1.623033 | 1.977577 | 2.422628 | -6.044080 | 0 |
| 39556 | -3.033797 | 2.481150 | 4.040309 | 2.182230 | 0.572183 | -2.170216 | -1.149011 | -0.120504 | -0.169341 | -0.990947 | -0.925397 | 3.151864 | 4.050267 | -2.154015 | -4.921109 | -1.739834 | -1.198557 | -0.667204 | 2.852070 | -0.730347 | -4.514946 | -0.815265 | -0.964336 | 1.317882 | -2.381280 | 5.823018 | -3.934089 | -2.095632 | 0.559665 | 1.846420 | 0.564035 | 2.513947 | 2.733024 | -0.238151 | 3.877495 | 0.279087 | -0.571441 | -0.319963 | 1.318343 | -3.030699 | 0 |
| 15721 | 2.017570 | 0.655922 | -1.211856 | 1.903444 | -1.168307 | -0.294108 | -0.545714 | -0.801814 | 2.322762 | -0.923089 | 1.406304 | -1.357111 | 3.017084 | 0.138301 | -3.114844 | -2.445253 | -1.305372 | -0.975139 | 0.804120 | -0.278339 | -3.661580 | 1.881718 | 2.016438 | -0.343973 | -1.162575 | 1.123767 | -0.563102 | -0.562220 | -0.270868 | 1.969972 | -2.508095 | 2.539157 | 1.325100 | -1.451506 | 2.862894 | -4.018710 | -1.155259 | -0.992527 | -1.497569 | -2.839966 | 0 |
| 11015 | 0.198955 | -2.549694 | 5.299876 | 1.000761 | -2.405681 | -3.235398 | -1.956365 | -0.327976 | 2.693341 | 0.756027 | -4.251156 | -0.300755 | 5.750176 | 1.504982 | -3.889981 | -3.773791 | -0.779254 | -0.708166 | 1.321059 | 5.888648 | -4.897954 | -0.240508 | -3.457808 | -5.199564 | 2.633569 | 0.297714 | 3.168034 | -1.514900 | -4.022257 | -3.356023 | -0.072776 | -3.871268 | -5.078766 | 2.741022 | 1.207946 | 4.380435 | 0.585061 | -6.322564 | 2.554262 | -3.306526 | 0 |
| 23365 | 0.059815 | -2.987197 | 4.633877 | 3.646574 | -4.276161 | -2.942109 | -3.498196 | -0.728207 | 2.475321 | 0.156005 | -1.921937 | 0.087871 | 4.986004 | -0.546296 | -6.342281 | -6.940053 | -0.168982 | -0.273233 | 5.535840 | 6.237346 | -9.134707 | 1.719019 | -2.493782 | -1.451272 | 0.311939 | -0.817561 | -1.138631 | -0.529814 | -0.246380 | 3.808747 | -3.662523 | -0.342636 | 0.110014 | 1.935277 | 8.268389 | -1.013531 | -0.448724 | -4.954901 | 0.259253 | -5.975593 | 0 |
| 2611 | 1.129344 | 5.700307 | 6.359979 | -3.163872 | 1.857215 | 1.313531 | -0.704013 | -3.386419 | 1.571679 | -4.091445 | 0.933413 | 0.072767 | 3.114912 | -4.203455 | -3.210593 | -3.519948 | -2.606892 | 1.765552 | -3.690099 | -5.414028 | -4.660995 | -1.913063 | -5.290676 | -3.428609 | -0.238750 | 9.907899 | -1.344391 | -5.304154 | 3.840166 | 0.034596 | 3.489011 | -5.149316 | 5.229540 | -6.188425 | 1.033047 | 1.000291 | 0.002547 | 0.670466 | 1.147707 | 4.227111 | 0 |
| 35605 | 1.432897 | -0.392184 | -0.776287 | -1.757397 | -0.508492 | 0.182788 | 0.573109 | 0.562698 | -0.014766 | -0.895993 | -0.373249 | 1.603005 | -0.662452 | -0.108861 | 1.060334 | -0.441252 | 1.564766 | 1.288230 | -0.826622 | 0.801976 | -0.031823 | 1.206045 | 1.245291 | -0.943074 | 1.236779 | -1.775025 | 1.052297 | 0.348392 | -0.072835 | -0.477059 | -1.337920 | -3.085089 | -2.299878 | -0.092188 | -1.295005 | 0.449221 | 1.455428 | -0.150809 | -0.053970 | 2.160198 | 0 |
| 38236 | 0.024271 | 0.268851 | 7.720791 | 0.075049 | -1.637084 | -1.795028 | -0.942213 | -3.462507 | 0.429371 | 2.193490 | -4.039552 | 1.756419 | 1.810758 | -1.833043 | -1.651406 | -6.760437 | -1.114332 | 0.215191 | 4.805633 | 1.648984 | -5.518833 | -0.245406 | -6.858156 | -1.295383 | 2.523473 | 2.453807 | 3.606201 | -0.531200 | -3.153716 | -4.117771 | 0.744265 | -6.640031 | -3.387048 | 3.927590 | 2.432549 | 5.047284 | -0.324327 | 1.308506 | 3.824518 | -2.687547 | 0 |
| 38379 | -0.828157 | -1.519533 | 2.442742 | 2.001547 | -1.178468 | -1.250156 | -2.560459 | -2.059534 | 2.541391 | 1.181373 | -1.320246 | -2.687167 | 5.557231 | 1.063416 | -5.994787 | -4.178627 | -3.300466 | -0.215607 | 1.009783 | 1.720896 | -5.137258 | 0.513313 | -1.514665 | -1.467573 | -0.665134 | 1.964168 | 0.589795 | -1.633797 | -2.328169 | 0.207927 | -0.314720 | 2.680102 | 1.202007 | -0.073791 | 3.485122 | 0.702715 | -1.366252 | -3.195055 | 1.124557 | -5.198514 | 0 |
| 30705 | -6.438882 | -6.083074 | 3.593711 | -1.536535 | 0.937110 | -2.383012 | -3.383262 | 2.529188 | -2.130789 | 3.513289 | -4.457682 | 0.194991 | 4.499246 | 3.478224 | -4.539376 | 2.701115 | -2.969486 | 2.110380 | -2.065720 | 3.714698 | 0.054895 | -2.189601 | -1.036144 | -0.260131 | -0.369723 | -0.795285 | 0.005542 | -2.378946 | -2.852419 | -0.681552 | 6.096518 | 5.772267 | 0.500669 | 0.854948 | 0.639783 | 9.746722 | 0.696395 | -7.446648 | 3.536391 | -2.239111 | 0 |
| 22619 | -2.331675 | 0.460116 | 1.324778 | -0.864949 | 0.291483 | -0.162441 | -1.082958 | -1.709996 | -0.039612 | -0.315726 | -0.882888 | 2.691032 | 2.890683 | -1.401227 | -4.426105 | -3.574258 | -1.121629 | 2.539114 | 0.798263 | -0.706196 | -3.614895 | 1.191435 | 0.416476 | 0.755685 | -1.297036 | 2.196330 | -1.132359 | -1.073943 | -0.340475 | 0.945906 | -1.675524 | -0.365491 | 1.221594 | -0.350954 | 1.283180 | 1.505271 | 0.893404 | 1.542937 | 2.021410 | -1.373481 | 0 |
| 14848 | 2.286318 | -0.657031 | -2.337108 | 2.410205 | -3.587670 | 0.659855 | 0.262662 | -4.486090 | 3.231968 | 2.123827 | 3.828541 | -1.825839 | 4.500223 | 0.694096 | -3.350865 | -5.036982 | -5.019413 | -2.689089 | 4.799433 | -0.442111 | -4.004076 | 2.496213 | 0.117812 | -2.604407 | -1.260637 | -1.030602 | 4.579041 | 1.091056 | -2.091526 | 0.528941 | -6.920426 | -1.337117 | -0.384344 | 3.656953 | 2.485581 | -4.903601 | -1.962949 | 3.506439 | 0.436992 | -7.572937 | 1 |
| 37648 | 1.873016 | 1.078503 | 4.813268 | -2.153427 | -0.286118 | 0.360076 | -0.335508 | -3.853423 | 0.819782 | 1.326446 | -2.000280 | -0.493579 | 1.133127 | -1.217099 | -0.775266 | -5.279191 | -2.347577 | 1.233497 | 0.540525 | -1.785450 | -3.680377 | 0.283004 | -4.774374 | -1.444100 | 2.075230 | 2.969183 | 4.202131 | -1.396807 | -2.284167 | -3.962096 | 1.990839 | -5.035232 | -1.245556 | 0.139309 | 0.487278 | 3.777258 | -0.606367 | 1.848805 | 2.444018 | 0.009816 | 0 |
| 18337 | 1.052733 | -0.184181 | 5.338061 | 1.343450 | -2.868400 | -0.938371 | -1.901907 | -2.851551 | 0.534488 | 0.514035 | -1.632994 | 1.516617 | 0.343148 | -3.078319 | -2.710108 | -8.199172 | 0.511881 | 1.232626 | 6.005861 | 1.925749 | -7.818113 | 1.856933 | -4.309517 | 1.347263 | 0.952360 | 0.473365 | -0.244097 | 0.293406 | 0.650462 | 2.193765 | -2.398908 | -3.701518 | 0.709212 | 1.574827 | 6.966137 | -0.306793 | -0.544122 | 1.456087 | 0.785048 | -2.737362 | 1 |
| 36235 | -0.382826 | -0.991667 | 2.616497 | -0.568539 | -0.521365 | -3.082321 | 0.094496 | 5.097471 | -2.102231 | -1.224248 | -2.250829 | 5.170991 | 0.418953 | -0.077720 | 0.932226 | 3.238875 | 2.854922 | -0.404563 | 0.875070 | 3.108985 | -0.678489 | -0.533786 | 1.272552 | -0.019790 | 0.931199 | -0.665689 | -1.957649 | -0.832353 | 0.614459 | 0.966504 | 2.288354 | -0.036224 | -2.532915 | 0.597288 | 1.456177 | 2.829239 | 1.628265 | -5.049029 | -0.139222 | 2.074390 | 0 |
| 18886 | 0.274169 | -1.628993 | -0.778907 | -3.212376 | -1.059530 | 0.279253 | 1.132979 | 2.309243 | -3.677208 | 0.619283 | 1.687866 | 5.507566 | -2.308692 | -0.896127 | 2.640085 | 2.123766 | 0.403036 | 1.249811 | 1.926445 | -0.923230 | 0.558974 | 1.132078 | 1.961543 | 1.859652 | -0.161489 | -3.318922 | 0.444411 | 0.683503 | 2.328770 | 2.868475 | -0.171365 | -1.305081 | 0.156442 | 1.138545 | 1.480168 | 1.213203 | 1.323510 | 1.673054 | -0.072915 | 2.295495 | 0 |
| 28002 | -2.641170 | 0.859378 | -5.188114 | -2.712596 | -0.420206 | 3.683387 | 1.580699 | -1.169112 | -2.510808 | -0.964908 | 9.257905 | 4.284578 | 0.383575 | -2.344422 | -0.714936 | 3.116846 | -4.343162 | 0.412657 | 1.160515 | -5.907670 | 2.114208 | -0.029619 | 2.766553 | -0.021538 | -5.110715 | -0.323425 | -1.688584 | -0.166619 | 7.241512 | 7.660523 | -5.216664 | -0.572112 | 7.591753 | -0.125988 | 0.624900 | -4.994668 | 0.780199 | 7.418895 | -0.131303 | 0.107965 | 0 |
| 3355 | -4.977099 | -0.952343 | 0.741048 | 3.749357 | 0.001823 | -2.810935 | -0.499805 | 1.118378 | -2.143390 | 1.989371 | -4.360019 | 4.618693 | -1.295679 | -0.649124 | -1.190917 | -1.648667 | 3.761408 | 0.400956 | 6.177814 | 4.027774 | -0.651711 | 0.820540 | 1.234430 | 5.389936 | -0.719140 | -1.965964 | -3.162521 | 3.163831 | -2.938557 | -0.308828 | -2.663752 | 2.189223 | -3.353544 | 5.530906 | 1.271631 | 1.768116 | 0.807515 | 2.208090 | 1.943638 | -4.177751 | 0 |
| 31107 | 2.903485 | -1.240829 | 5.041397 | -1.044704 | -4.161486 | -1.262780 | -0.506679 | -2.850328 | 0.648377 | 2.260955 | 0.458923 | 2.915857 | 4.481675 | -1.186705 | -2.801983 | -6.346400 | -4.432246 | -0.817882 | 5.983520 | 0.214920 | -7.900524 | 1.941034 | -3.657162 | -2.589240 | 1.091626 | 0.607371 | 5.311974 | -1.176843 | -1.498291 | -0.028402 | -1.759536 | -5.192133 | -1.395217 | 3.327630 | 5.784308 | 1.649303 | -0.924436 | 0.682714 | 2.210882 | -4.357675 | 0 |
| 21272 | -6.612191 | 1.751127 | 3.850205 | 7.518795 | 2.842173 | -6.562192 | -2.974898 | 2.420955 | -0.069783 | -0.006304 | -11.384285 | 4.258754 | 2.961779 | -0.595493 | -7.840477 | -5.161095 | 6.369387 | 1.746707 | 4.717341 | 5.508933 | -6.254668 | 2.188386 | 4.203484 | 9.432864 | -1.522188 | 4.530465 | -8.931097 | 0.987020 | -7.598015 | -2.062897 | 0.417342 | 10.621078 | -3.888526 | 1.350227 | 3.602107 | 3.225435 | 0.122381 | -3.159808 | 1.814144 | -6.384603 | 0 |
| 30721 | -3.404173 | 2.449586 | 0.697544 | 7.891278 | 1.449616 | -2.451370 | -2.042021 | -0.791717 | 3.170024 | -1.606224 | -4.874094 | -3.166212 | 0.507750 | -0.557205 | -4.160640 | -3.901476 | 4.949447 | -0.846128 | 1.983118 | 4.525733 | -1.938174 | -0.165218 | 0.039214 | 3.221099 | -0.898143 | 2.788036 | -6.513672 | 1.716401 | -2.657915 | -0.830789 | -2.815700 | 4.597724 | -0.984419 | 0.512575 | 0.763521 | -3.356078 | -0.779467 | -1.097666 | -0.572308 | -4.486963 | 0 |
| 8532 | 1.345066 | -2.205403 | 6.054068 | -2.767798 | -1.820600 | -1.628920 | -1.151630 | -0.926633 | -0.099164 | 2.104828 | -2.870119 | 1.640388 | 3.510284 | 0.303575 | -1.793599 | -3.496212 | -2.860038 | 0.900516 | 1.290931 | 1.278455 | -4.889581 | 0.293136 | -3.920645 | -2.757036 | 2.472684 | 0.802385 | 4.781637 | -2.178182 | -2.722599 | -2.760382 | 3.044427 | -3.956536 | -2.595371 | 1.367230 | 2.402145 | 6.398323 | 0.097163 | -3.027195 | 2.844607 | -0.868606 | 0 |
| 14824 | -3.012131 | -4.771158 | -0.873739 | -0.283531 | -1.865989 | -2.219165 | -0.290609 | 3.024712 | -2.994476 | 2.759058 | -1.729414 | 5.656278 | 0.614076 | 1.433029 | -0.592258 | 1.155419 | 0.840672 | 0.860888 | 4.209795 | 4.112773 | -0.273975 | 1.602644 | 3.136934 | 2.115911 | -0.247386 | -5.631704 | 0.496736 | 1.937775 | -1.950709 | 1.410957 | -2.066688 | 1.407957 | -3.415033 | 5.043880 | 1.503223 | 3.427350 | 1.666495 | -1.084971 | 1.683620 | -2.780175 | 0 |
| 10697 | 0.935959 | 9.273840 | 4.053711 | 0.097732 | 3.396549 | 0.143430 | 1.768525 | -0.972799 | -0.417654 | -4.845670 | 0.013919 | 3.430930 | -1.850696 | -5.676595 | 0.913962 | -1.065758 | 2.407259 | -0.194442 | -0.395353 | -6.217209 | -1.692070 | -1.461674 | -1.938899 | 2.323998 | -0.973704 | 9.977687 | -5.101097 | -2.238675 | 3.374913 | -0.485722 | 2.511387 | -2.545585 | 3.009911 | -4.397679 | 0.054069 | -2.313479 | -0.259906 | 4.742675 | -0.576364 | 4.705849 | 0 |
| 29124 | -3.898090 | 1.912384 | -0.574357 | 4.576335 | 2.657376 | -2.907905 | 0.604876 | 5.611180 | -6.290593 | 1.676738 | -1.927993 | 5.784844 | -5.403316 | -1.648626 | 1.983117 | 4.687986 | 3.795077 | -1.479481 | 6.278561 | -2.329022 | 1.048613 | 0.451855 | 5.337246 | 13.742698 | -3.984433 | 1.118918 | -8.949141 | 2.368378 | 0.587422 | 4.612206 | 4.342883 | 14.358041 | 3.764584 | 0.477207 | 6.135566 | -1.509435 | -2.249153 | 2.492728 | -2.499642 | -2.079096 | 0 |
| 12594 | 0.933196 | 3.023748 | 7.300429 | 6.572631 | -1.365063 | -3.106873 | -2.790060 | -3.641937 | 2.006128 | 0.917386 | -4.934177 | -0.642300 | 1.499961 | -3.488422 | -5.561888 | -11.178589 | 1.077363 | -0.642052 | 8.406349 | 1.148636 | -11.254306 | 2.414271 | -3.982770 | 6.026883 | -0.212472 | 5.518345 | -3.460121 | 0.236672 | -2.868802 | 0.677885 | -0.472422 | 3.532348 | 1.408595 | 0.559405 | 10.174473 | -2.164675 | -3.472862 | 1.273217 | -0.063451 | -7.105633 | 0 |
| 19344 | -3.004504 | 8.987109 | 1.784967 | 2.889290 | 5.020107 | 0.590063 | 0.116758 | -2.354865 | -0.564976 | -3.455149 | -0.615991 | 1.476282 | -1.591934 | -5.351462 | -2.867011 | -2.604642 | 1.751400 | 1.131433 | 0.363237 | -7.089803 | -2.076309 | -0.596044 | 0.293054 | 7.431610 | -4.001083 | 10.653224 | -8.812507 | -1.284251 | 2.364713 | 1.568712 | 1.598780 | 5.278946 | 6.828839 | -5.104168 | 1.673383 | -3.736353 | -1.530547 | 6.844000 | -0.597835 | 0.790179 | 0 |
| 36619 | 0.551989 | -0.256166 | -0.879237 | 1.848576 | -1.958090 | -0.031529 | 0.427883 | -2.724114 | 1.338676 | 2.004113 | 1.509085 | -0.254548 | 2.221229 | 0.297631 | -1.535724 | -2.950925 | -2.728472 | -1.902071 | 4.020947 | -0.077893 | -1.933157 | 1.204102 | -0.387704 | -0.821483 | -0.659633 | -0.590819 | 2.922099 | 1.184585 | -2.087095 | -0.668503 | -3.964369 | -1.029498 | -1.192296 | 3.598609 | 1.106246 | -2.021353 | -1.193404 | 3.053623 | 0.970229 | -5.130268 | 1 |
| 18271 | -1.939785 | 1.258004 | 3.050736 | -0.230640 | 1.408594 | -1.529679 | -2.379458 | 0.181952 | 0.918978 | -2.530513 | -3.358092 | 1.790094 | 4.078843 | -1.340494 | -6.248568 | -3.570305 | 0.800259 | 3.311782 | -1.870701 | 0.447370 | -5.488758 | 1.107794 | 1.270776 | 1.097730 | -0.969422 | 4.602375 | -4.223476 | -2.844582 | -0.327345 | 1.351230 | 0.989402 | 2.538486 | 1.849870 | -4.065086 | 2.412635 | 1.953404 | 1.032757 | -3.298489 | 0.829201 | 0.275411 | 0 |
| 27365 | -4.783096 | 4.685502 | 3.969437 | 4.139468 | 3.119928 | -3.228184 | -2.783146 | -1.689988 | 2.379210 | -2.966655 | -6.886867 | 2.369641 | 5.527742 | -2.603404 | -9.812259 | -7.386465 | 2.697024 | 3.074418 | 0.641426 | 0.919211 | -7.631226 | 1.622948 | 1.812140 | 4.205023 | -2.213448 | 8.758623 | -7.471303 | -1.976686 | -3.776611 | -1.043122 | -1.160087 | 4.789587 | 0.357861 | -2.776980 | 2.065861 | 1.010750 | 0.538583 | -0.655503 | 2.160584 | -3.580627 | 0 |
| 10832 | -1.845534 | 1.000472 | -0.934954 | -1.021070 | 2.016448 | -1.389452 | -0.772672 | 1.384395 | -1.073455 | -1.726510 | -4.587207 | 5.316867 | 0.768213 | -1.156846 | -3.901998 | -3.287886 | 3.782379 | 5.073490 | -0.727101 | 0.133745 | -3.593809 | 3.969265 | 6.322847 | 5.475863 | -0.866970 | 1.033392 | -4.180248 | -0.031699 | -2.500983 | 0.460059 | -1.267094 | 3.574224 | -1.517323 | -2.532185 | 0.385773 | 1.956579 | 2.237089 | 0.289043 | 0.632390 | 1.136851 | 0 |
| 9791 | 0.160180 | 0.505597 | 0.950054 | -3.644409 | -0.914943 | 0.160194 | -0.467043 | -1.306020 | 1.862213 | -3.521670 | -0.269551 | 4.108551 | 4.561869 | -1.619636 | -4.246515 | -4.097801 | 0.355770 | 3.716370 | -2.191416 | 1.191419 | -4.182316 | 1.833092 | 0.913669 | -4.993187 | 0.701528 | 1.160782 | 0.709051 | -2.102511 | 0.812913 | 0.299934 | -4.595897 | -7.898936 | -1.984921 | -1.338168 | -1.630456 | 1.408519 | 3.626491 | -0.717138 | 2.127883 | 2.353895 | 0 |
| 37269 | -1.168136 | 1.364897 | 0.307025 | 0.543357 | 1.929949 | -3.154096 | -0.322949 | 5.794710 | -2.747870 | -2.474589 | -3.451660 | 6.218637 | -0.158662 | -1.023802 | -1.533051 | 2.127497 | 4.506521 | 1.745644 | 0.126561 | 0.291462 | -2.407161 | 1.969135 | 6.593809 | 6.405490 | -1.514979 | 1.820483 | -6.962728 | -0.844801 | 0.096861 | 3.100359 | 2.637684 | 7.715523 | 0.352047 | -3.363951 | 3.264467 | 0.637833 | 1.067040 | -3.611792 | -1.772873 | 2.013462 | 0 |
| 15229 | -3.032493 | -0.188498 | 1.635762 | -1.030664 | 2.429258 | -0.874784 | -2.210980 | 1.454548 | -1.344084 | -0.685114 | -3.041096 | 1.277232 | 1.614693 | -0.293355 | -4.198830 | -0.462560 | 0.296997 | 3.589844 | -2.537970 | -0.843630 | -2.468167 | 0.583273 | 2.221026 | 3.880309 | -1.557387 | 2.901338 | -4.413430 | -2.204949 | -0.147569 | 1.866867 | 3.816405 | 6.455370 | 3.388585 | -4.254778 | 2.171826 | 3.275388 | 0.412083 | -2.849114 | 0.389583 | 0.948109 | 0 |
| 17666 | -1.736669 | 3.929785 | -0.005669 | 0.502542 | 2.582312 | 0.007631 | 0.212890 | 0.139546 | -1.029161 | -2.520719 | -1.441173 | 2.981098 | -1.827338 | -2.872731 | -0.972162 | -1.245099 | 3.260722 | 2.221763 | -0.184533 | -2.280020 | -0.833185 | 0.799423 | 2.158307 | 4.572311 | -1.498013 | 3.556399 | -5.264013 | 0.016858 | 1.176266 | 1.139474 | -0.290248 | 1.763153 | 1.767230 | -2.572592 | 0.065995 | -1.190228 | 0.771645 | 3.155925 | -0.345231 | 2.085297 | 0 |
| 39287 | -2.947121 | 1.883465 | -0.747108 | 2.802790 | 1.164198 | -1.475600 | 1.382059 | 3.160474 | -4.262554 | 0.496407 | -0.120784 | 5.424533 | -4.418665 | -2.051207 | 2.656939 | 3.259952 | 3.329644 | -1.387377 | 5.263638 | -1.066607 | 1.940109 | -0.235594 | 2.565835 | 7.469908 | -2.344114 | -0.073635 | -5.351554 | 2.444343 | 1.549588 | 2.857541 | 0.271007 | 4.989017 | 1.195776 | 2.362324 | 2.390468 | -1.752582 | -0.443564 | 3.950097 | -0.985432 | -0.742676 | 0 |
| 29787 | -2.215241 | -3.060838 | 1.991694 | -2.406899 | 0.884310 | -2.267599 | -2.644941 | 1.886075 | -1.745479 | 1.344196 | -5.384473 | 3.747627 | 3.732387 | 1.066660 | -5.783695 | -2.870231 | -0.396447 | 5.006161 | -0.883379 | 1.034013 | -5.486551 | 3.278547 | 4.236984 | 4.314674 | -0.465211 | 0.294844 | -1.412324 | -1.923152 | -3.841329 | 0.588590 | 3.108079 | 6.505072 | -0.230786 | -2.390099 | 3.435992 | 6.484770 | 1.042696 | -4.324503 | 1.740951 | -0.980324 | 0 |
| 14555 | 0.554669 | 1.442305 | 1.858539 | -5.308874 | -0.208186 | 2.795322 | -0.069601 | -3.406468 | 0.781246 | -2.370226 | 3.347319 | 1.274253 | 2.520276 | -2.465885 | -1.951607 | -2.436711 | -3.318240 | 2.880509 | -3.183558 | -3.471533 | -1.989874 | -0.522034 | -3.088510 | -5.283357 | -0.042093 | 3.007306 | 2.032143 | -3.102921 | 3.995207 | 1.151781 | -1.215720 | -8.189511 | 3.152610 | -2.769844 | -1.217662 | 1.106030 | 1.785767 | 2.293668 | 1.900877 | 3.725302 | 0 |
| 36164 | -2.218269 | 1.629821 | 2.404382 | 1.581095 | 0.871276 | -2.597082 | -0.560123 | 2.961192 | -1.137970 | -1.959178 | -1.768266 | 4.060076 | 1.595375 | -1.413541 | -2.219217 | 1.138991 | 1.999903 | -0.257116 | 1.310532 | 0.882751 | -2.176060 | -0.519288 | 1.442108 | 2.057732 | -1.383762 | 3.159520 | -4.969411 | -1.262631 | 0.993045 | 2.100706 | 1.263544 | 3.059118 | 0.881335 | -0.895585 | 2.530626 | 0.379111 | 0.551479 | -2.478544 | -0.130772 | -0.072461 | 0 |
| 36185 | 2.841434 | 0.357616 | 1.267348 | -1.391025 | 1.223632 | -1.259726 | -0.278904 | 0.227615 | 1.859471 | 0.736700 | -2.449653 | -2.399179 | 4.839069 | 2.708765 | -2.378446 | -0.090761 | -3.460023 | -0.438853 | -4.017908 | -1.950468 | -2.183423 | 0.786152 | 1.628796 | -1.546302 | 0.877233 | 3.759739 | 3.322671 | -3.108545 | -5.134678 | -4.568886 | 5.181728 | 4.114950 | -1.757208 | -3.276502 | -0.836977 | 3.338356 | -1.451804 | -4.836154 | 0.346937 | -0.635917 | 0 |
| 34003 | -1.683566 | 5.678574 | 0.110332 | 0.166250 | 5.718339 | 0.445674 | 0.340780 | 3.023883 | -4.542294 | -1.902281 | -0.459095 | 2.063459 | -5.150511 | -3.144200 | 1.253230 | 3.887180 | 2.146572 | 1.664323 | -1.972682 | -8.100417 | 1.107113 | -0.388911 | 3.875471 | 11.307534 | -3.820304 | 6.899562 | -9.326826 | -1.467843 | 3.470722 | 3.923666 | 8.089716 | 12.420885 | 8.552835 | -7.733476 | 3.357331 | -1.392154 | -2.047835 | 2.714668 | -3.181505 | 4.381410 | 0 |
| 14807 | -1.447879 | -2.745134 | 5.858748 | -0.148781 | -1.196952 | -3.335568 | -1.638803 | 1.705941 | -0.469868 | 1.803947 | -3.693172 | 1.733366 | 4.195433 | 1.281840 | -2.413588 | -0.125844 | -1.605313 | -0.760169 | 1.526707 | 3.649445 | -3.111007 | -1.400965 | -3.012104 | -2.279645 | 1.413804 | 0.873674 | 1.707869 | -2.034402 | -2.587686 | -1.871275 | 3.799484 | -0.223210 | -2.493107 | 2.210613 | 2.326240 | 6.250968 | 0.107246 | -5.981287 | 2.485177 | -2.180135 | 0 |
| 34564 | -1.524946 | 1.390515 | -2.665649 | -1.331508 | -1.820057 | 3.018503 | 2.154331 | -3.035052 | -0.349596 | -1.697809 | 5.898940 | 3.646823 | -2.077183 | -3.245421 | 2.168488 | -0.504217 | 0.331129 | 0.028214 | 2.833547 | -0.715212 | 2.747198 | -0.790117 | -2.083147 | -3.786069 | -0.915654 | -2.392061 | 0.847122 | 2.183514 | 5.423601 | 2.622640 | -8.695744 | -11.158371 | 0.406416 | 4.220731 | -3.087842 | -4.075073 | 2.530839 | 8.387347 | 1.397740 | 1.219563 | 0 |
| 35066 | -5.552474 | -1.598892 | 6.239314 | 2.080467 | -0.664997 | -1.440700 | -2.798065 | -3.425266 | -1.168376 | 3.818884 | -3.160756 | 1.077067 | 2.132174 | -1.486056 | -4.824178 | -5.545607 | -2.551886 | 1.004814 | 5.989725 | 1.591746 | -4.608870 | -1.071936 | -5.901425 | 2.494423 | -0.962544 | 2.011867 | -0.506102 | -0.028017 | -1.666723 | 0.043613 | 0.546337 | 0.429497 | 1.882881 | 4.224996 | 4.717206 | 4.645924 | -1.060984 | 2.203938 | 4.161072 | -6.089375 | 0 |
| 16253 | -2.825120 | -3.233723 | 0.550051 | -1.459855 | -0.382636 | -0.755402 | -2.410831 | -0.676389 | -0.249866 | 0.938978 | -3.569641 | 2.349220 | 2.665611 | 0.370056 | -5.199615 | -4.661241 | 0.334378 | 4.806043 | 0.235245 | 2.724121 | -4.142597 | 2.829797 | 2.051025 | 1.663369 | -0.104329 | -1.883144 | -0.489689 | -0.125459 | -2.416966 | 0.789238 | -1.992278 | 0.921467 | -0.957953 | 0.170111 | 1.489989 | 3.962206 | 1.878119 | -1.210575 | 2.277826 | -1.655109 | 0 |
| 23456 | -2.994231 | -1.324617 | 0.391940 | -1.751654 | 0.797653 | -0.588132 | -2.044996 | 0.521712 | -0.887207 | -0.128921 | -0.741078 | 2.682954 | 4.528141 | 0.041364 | -6.133129 | -1.307700 | -2.376924 | 3.306548 | -1.178602 | -1.044040 | -3.931513 | 1.598223 | 3.230300 | 1.879747 | -2.501002 | 1.932840 | -2.467909 | -2.423401 | -0.090556 | 3.140811 | 0.711712 | 5.145558 | 3.464385 | -2.760763 | 2.839658 | 2.468803 | 0.697924 | -1.978058 | 1.203911 | -1.320031 | 0 |
| 25467 | -3.069126 | 0.241014 | -1.888907 | -2.078492 | 0.408096 | 0.755020 | -0.708428 | -0.564522 | 0.212806 | -2.211612 | 2.241825 | 3.539661 | 4.667946 | -0.961266 | -5.477926 | -0.737715 | -1.965351 | 2.773470 | -1.684938 | -1.363464 | -2.058019 | 1.101413 | 3.371338 | -1.409577 | -2.942788 | 1.728838 | -2.266284 | -1.915427 | 2.121504 | 3.620705 | -3.761789 | -0.036453 | 2.986549 | -1.857162 | -0.241339 | -0.484775 | 2.097582 | 0.763668 | 1.360656 | -0.228577 | 0 |
| 36538 | -3.657594 | 3.097886 | 1.796112 | 1.824225 | 2.571454 | -0.865604 | -0.211483 | -3.194941 | -1.721015 | 1.917422 | -5.865688 | 3.795089 | -2.369465 | -2.810914 | -2.164217 | -6.766226 | 2.811583 | 3.430688 | 4.550728 | -1.661863 | -2.893297 | 2.523307 | 0.608871 | 8.613270 | -0.704820 | 2.904482 | -2.498674 | 2.329694 | -4.820135 | -3.337304 | -1.024302 | 1.864749 | -1.891673 | 2.147856 | 0.403598 | 2.685526 | -0.074244 | 7.757939 | 2.907800 | -2.672526 | 0 |
| 21429 | 0.959780 | 0.314588 | 6.514919 | 2.268321 | -1.573278 | -2.639185 | -1.923541 | -2.295993 | 1.556171 | 1.610038 | -3.955122 | -0.087892 | 3.594088 | -0.930083 | -4.088333 | -6.848388 | -1.444309 | -0.541146 | 4.427524 | 1.605736 | -7.675278 | 0.963483 | -3.959128 | 0.511808 | 1.141326 | 3.437923 | 1.231296 | -1.187563 | -3.553104 | -1.899206 | 1.239244 | -0.157568 | -1.272066 | 1.349653 | 5.437425 | 2.292191 | -1.759824 | -1.627477 | 1.675790 | -4.660011 | 0 |
| 1524 | -1.348478 | -1.469401 | -1.469395 | 4.976958 | -2.329001 | -0.571910 | -3.003055 | -0.810132 | 2.679013 | -1.492850 | 1.269624 | -2.107911 | 2.701980 | -0.613713 | -6.161241 | -4.568480 | 0.870058 | 0.223730 | 3.106442 | 3.779839 | -5.389732 | 2.068291 | 1.645398 | 1.236214 | -2.554322 | -1.246226 | -5.160973 | 0.752174 | 2.335202 | 7.004286 | -6.226927 | 4.383583 | 4.175097 | -0.734970 | 6.387898 | -6.465921 | -0.642642 | -1.917402 | -2.122769 | -5.233406 | 0 |
| 3681 | -5.900330 | -1.912728 | 3.278969 | 3.012582 | 0.866233 | -2.739504 | -3.616333 | -0.831563 | -0.259942 | 2.109159 | -6.154210 | 1.093412 | 3.339310 | 0.094966 | -7.339233 | -5.255058 | 0.479294 | 2.956515 | 2.934760 | 3.362520 | -5.202389 | 1.252955 | 0.258548 | 4.939736 | -1.455513 | 1.568834 | -3.777508 | -0.047366 | -4.097126 | 0.205086 | 0.223642 | 6.303681 | 0.415747 | 1.196952 | 3.841653 | 4.206525 | -0.107335 | -1.607095 | 2.748055 | -5.639751 | 0 |
| 6069 | -2.570932 | 0.067817 | -2.475629 | 3.720938 | 2.266980 | -3.262857 | -0.243620 | 5.134360 | -0.798590 | -1.223141 | -5.288921 | 1.949745 | -1.372613 | 1.493636 | -0.319920 | 2.831556 | 6.463948 | 0.352650 | -0.483001 | 4.010634 | 1.788107 | 1.109427 | 6.451837 | 5.344831 | -0.444058 | -1.195993 | -5.875201 | 1.834457 | -3.251277 | -0.640109 | 0.457548 | 7.740749 | -3.677048 | -0.265801 | -1.304556 | 0.063791 | 1.047921 | -3.840493 | -1.485624 | -0.131259 | 0 |
| 37437 | 1.242583 | -1.615257 | -1.124984 | -6.568385 | -2.389451 | 3.835002 | 1.943843 | -2.355271 | -1.055478 | -0.613321 | 5.319346 | 2.821329 | -2.058199 | -1.574760 | 4.128320 | 0.499781 | -1.514316 | 1.940865 | -1.019368 | -0.937444 | 3.118258 | -0.749898 | -3.455533 | -6.816118 | 1.823032 | -4.488844 | 5.669116 | 0.424921 | 4.922884 | 0.717953 | -4.428749 | -14.071288 | -0.889022 | 2.487043 | -4.196741 | 1.009847 | 3.273884 | 4.855199 | 1.981025 | 5.107954 | 0 |
| 16121 | 9.736695 | 1.905465 | 5.746599 | -5.445060 | -3.792717 | 0.373171 | 1.947900 | -4.202219 | 1.281628 | 1.373251 | 1.751535 | 2.573421 | 3.543277 | -1.841475 | 1.000274 | -6.920399 | -6.173891 | -0.950893 | 2.917002 | -5.112819 | -8.321509 | 2.994510 | -4.228672 | -4.799217 | 3.496474 | 3.187929 | 10.514629 | -2.914852 | -2.224721 | -3.930258 | 1.342362 | -9.864847 | -3.030912 | 0.129746 | 3.742716 | 2.054883 | -1.560063 | 2.337748 | 1.444385 | 0.332080 | 0 |
| 31201 | -0.210564 | 0.155227 | 2.535274 | -2.913884 | -1.311327 | 1.704555 | 1.384166 | -0.931468 | -2.542154 | 0.962013 | 4.939373 | 2.229697 | -1.356689 | -1.949195 | 3.429441 | 2.792254 | -3.623359 | -1.768687 | 2.083201 | -3.034371 | 1.975984 | -3.101603 | -5.348993 | -2.986668 | -0.283150 | 0.357477 | 2.595833 | -0.859341 | 4.976494 | 1.816803 | 1.257188 | -6.108846 | 3.110321 | 2.227545 | 0.699907 | 0.868046 | -0.173193 | 3.534691 | 1.235881 | 1.672138 | 0 |
| 11996 | 2.555354 | 4.264820 | 6.319774 | 2.730460 | -2.281043 | 1.969891 | -1.772891 | -10.612647 | 4.676671 | 0.382291 | 0.434363 | -4.405126 | 1.617037 | -4.890951 | -4.601899 | -14.350521 | -3.171914 | 0.342882 | 5.689023 | -2.350576 | -9.457275 | 1.159135 | -9.635680 | -1.326948 | 0.682178 | 5.926132 | 2.430382 | -0.268549 | 0.047761 | -1.011449 | -4.703490 | -8.123385 | 3.030145 | 0.799516 | 5.380102 | -3.675522 | -2.776969 | 7.746123 | 1.901650 | -5.073469 | 0 |
| 20461 | 2.998797 | -0.513748 | 6.959741 | -2.088628 | -0.191726 | -3.421051 | -0.564362 | 1.269744 | 0.373539 | 1.572283 | -5.355842 | 0.969402 | 4.444402 | 1.574714 | -1.136346 | -1.301122 | -2.221803 | -0.637433 | -0.852903 | 0.672089 | -4.503879 | -0.103511 | -2.374259 | -2.267819 | 3.219590 | 3.638008 | 4.608499 | -3.435892 | -5.567697 | -5.974737 | 7.342034 | -0.401694 | -4.683727 | -0.553922 | 1.276928 | 7.666204 | -0.783898 | -6.540565 | 2.034245 | -0.029997 | 0 |
| 37463 | 3.052566 | -2.105544 | 1.177972 | -2.244719 | -2.568707 | -0.771466 | -0.326089 | 1.363491 | -0.725786 | 0.137629 | 0.605516 | 2.974513 | 1.447921 | -0.168192 | -0.593440 | -1.562325 | -0.877891 | 0.839010 | 1.654010 | 0.607584 | -4.312292 | 2.402427 | 1.257666 | -0.436254 | 0.780191 | -2.005296 | 1.724206 | -0.872463 | 0.667178 | 2.554754 | -0.253197 | -0.951996 | -0.326692 | -0.445928 | 4.278832 | 0.566852 | 0.373876 | -2.345528 | -0.632567 | 0.288391 | 0 |
| 34618 | 3.327028 | 0.285009 | 5.417746 | -0.378648 | -2.792663 | -1.269398 | -0.413724 | -1.573067 | 2.017781 | -0.481541 | -0.566344 | 0.359023 | 2.912646 | -1.118029 | -0.884436 | -4.076380 | -1.265168 | -1.329848 | 2.195241 | 1.765570 | -5.090198 | -0.183557 | -4.916895 | -4.856719 | 2.460753 | 1.669346 | 3.433956 | -1.635319 | -0.220291 | -1.429116 | -0.265084 | -6.958518 | -2.237995 | 1.222237 | 2.626588 | 1.032473 | -0.165975 | -2.184789 | 1.017450 | -0.675944 | 0 |
| 33770 | -4.693524 | 1.647137 | -0.603276 | 2.654234 | 3.183519 | -1.926159 | -1.700050 | 0.418241 | -1.304620 | -0.237189 | -6.196832 | 3.537725 | -0.329765 | -1.359817 | -5.116246 | -4.333657 | 4.313924 | 4.434499 | 1.563623 | 0.290973 | -3.443592 | 3.387042 | 5.592431 | 9.538936 | -2.273540 | 2.251896 | -6.944190 | 1.248147 | -3.746567 | 0.470600 | -0.817064 | 8.411205 | 0.108030 | -1.482873 | 1.789037 | 1.200970 | 0.630388 | 2.050858 | 0.754122 | -2.209490 | 0 |
| 25883 | 1.598488 | -3.399148 | 1.668204 | -8.178977 | -2.074138 | 1.598587 | -1.654889 | -1.474870 | 2.350275 | -3.038500 | 0.645194 | 1.737908 | 6.137868 | 0.244441 | -4.363999 | -3.508991 | -2.343655 | 5.748914 | -6.568968 | 2.075070 | -4.017161 | 1.394150 | -0.947720 | -9.889380 | 2.518306 | -1.374261 | 4.690334 | -4.113463 | 1.843402 | 0.381019 | -2.517727 | -11.064644 | -1.431327 | -3.218870 | -2.489239 | 4.981693 | 4.630513 | -4.905516 | 2.720728 | 5.094461 | 0 |
| 26126 | -0.465321 | 2.252442 | -0.409218 | 5.167867 | 1.127198 | -2.462475 | -0.617623 | 1.576818 | 1.185955 | -2.145553 | -5.178612 | 0.376735 | -2.391835 | -0.988631 | -0.784915 | -2.892375 | 7.315419 | 0.507760 | 1.850513 | 3.822997 | -1.718939 | 1.925648 | 3.003464 | 5.108709 | 0.439017 | 0.294608 | -5.767193 | 2.410154 | -2.366911 | -0.530869 | -2.227147 | 3.445466 | -3.125633 | -0.283831 | 0.830206 | -2.811786 | 0.334426 | -0.808843 | -1.784292 | -0.779989 | 0 |
| 35342 | 4.756877 | 3.594681 | 2.922937 | -2.372987 | 1.055049 | 0.681432 | 0.945944 | -2.939881 | -0.115312 | 0.465005 | -2.716613 | 0.598656 | -2.240619 | -2.198841 | 1.473977 | -5.700192 | 0.172474 | 2.116772 | 0.301797 | -4.703006 | -3.941156 | 2.716594 | -1.079296 | 3.432263 | 2.107577 | 3.539926 | 2.696632 | -0.494084 | -2.910458 | -4.050205 | 2.970012 | -1.975749 | -1.399736 | -2.501080 | 1.048899 | 1.699824 | -1.197464 | 4.079478 | 0.225181 | 2.265987 | 0 |
| 6252 | -4.149269 | -2.366523 | 6.773087 | 1.638794 | -1.039920 | -0.985698 | -4.342976 | -3.929690 | -0.729444 | 3.439397 | -3.795681 | -0.297397 | 1.876318 | -1.936211 | -6.503588 | -8.560758 | -2.235602 | 3.157269 | 5.392675 | 1.510927 | -7.887953 | 0.733645 | -5.449649 | 4.309498 | -0.849828 | 1.753508 | -1.651505 | -0.557390 | -0.971082 | 2.307486 | 1.027067 | 2.506550 | 4.194395 | 1.297619 | 8.062576 | 4.001140 | -1.491910 | 0.935523 | 2.927955 | -5.638436 | 0 |
| 5876 | -4.173311 | 5.712513 | 3.034943 | 6.488268 | 3.537053 | -4.130565 | 0.093758 | 3.388791 | -2.591008 | -1.252228 | -4.422244 | 4.585222 | -1.756896 | -2.969257 | -1.216978 | 1.252955 | 4.764317 | -2.120136 | 4.942439 | -1.064233 | -1.499282 | -0.936954 | 2.212322 | 9.681636 | -3.055379 | 6.870977 | -9.775303 | 0.550517 | -0.847121 | 0.994921 | 3.353212 | 9.820637 | 1.643411 | -0.252133 | 3.969619 | -1.463031 | -1.713268 | 1.232281 | -0.986763 | -2.382961 | 0 |
| 31691 | 1.398985 | -1.824803 | 1.579816 | -0.128917 | -3.179667 | -1.690265 | -0.067869 | 1.735130 | -0.492432 | 0.087553 | 1.522730 | 3.625593 | 2.588484 | -0.256447 | -0.903571 | -0.192001 | -1.121808 | -1.490162 | 3.709541 | 1.972005 | -3.545187 | 0.845556 | 0.170726 | -1.874807 | 0.010374 | -1.595701 | 1.007660 | -0.541269 | 1.266894 | 2.966055 | -1.720924 | -1.652910 | -0.633222 | 2.045717 | 4.231642 | -0.516769 | 0.274404 | -2.365931 | -0.152295 | -1.795718 | 0 |
| 14430 | -4.791552 | 3.364564 | 4.967456 | 10.089220 | 0.826062 | -1.919279 | -4.132149 | -6.071716 | 5.402838 | -1.036240 | -6.884910 | -6.184189 | 0.766129 | -2.729514 | -7.178512 | -11.414384 | 4.838909 | 0.347878 | 4.558306 | 5.881162 | -5.896514 | -0.609193 | -6.282119 | 2.837659 | -0.142914 | 5.010719 | -6.583996 | 2.054596 | -3.050883 | -1.866392 | -4.829132 | 0.493597 | 0.075415 | 1.780495 | 2.733149 | -3.196443 | -1.508169 | 1.687669 | 1.365397 | -7.230568 | 0 |
| 38547 | -1.553839 | -5.513925 | -1.346778 | -2.589369 | -2.262152 | -3.063925 | -0.754665 | 4.150054 | -0.709283 | -0.394536 | -4.297835 | 7.207249 | 3.535001 | 2.138938 | -2.769325 | -0.689163 | 3.590195 | 3.739397 | 0.238030 | 7.601985 | -2.238016 | 3.553596 | 5.875584 | -1.960597 | 1.857395 | -6.664827 | 1.148319 | 0.801002 | -3.425742 | -0.162379 | -4.573619 | -3.115590 | -8.033150 | 3.193936 | -1.459737 | 5.076228 | 4.886828 | -5.513062 | 2.165110 | 0.234651 | 0 |
| 7550 | -2.817020 | -2.943859 | 1.895166 | -3.477291 | 0.586753 | 0.458817 | -1.108814 | -0.359207 | -1.535210 | 1.908308 | -1.441343 | 0.467229 | 0.976356 | 1.027242 | -0.854899 | 0.653988 | -2.084000 | 2.558603 | -2.119233 | 0.409149 | 1.050470 | -1.211303 | -2.054752 | -1.366344 | 0.656970 | -0.893565 | 2.136793 | -1.205865 | -0.664953 | -1.373248 | 2.820161 | -1.421476 | 0.074668 | 0.485082 | -1.636429 | 6.271935 | 1.188241 | -1.176646 | 2.816877 | 1.105352 | 0 |
| 2788 | -4.335772 | -5.121719 | -0.055006 | 0.798858 | -1.791265 | -2.288616 | -1.695804 | 1.512113 | -2.099418 | 3.130580 | -2.817173 | 4.314182 | 1.855299 | 1.303278 | -3.343792 | -1.526878 | 0.372846 | 1.830274 | 4.601556 | 4.786493 | -2.252645 | 1.960335 | 2.367713 | 2.787462 | -0.746748 | -4.820981 | -0.391631 | 1.763482 | -2.653345 | 1.774284 | -2.694146 | 2.684466 | -2.282416 | 4.762860 | 2.779916 | 3.606908 | 1.309685 | -1.154687 | 2.286411 | -4.695814 | 0 |
| 27472 | 1.504463 | 1.138884 | 0.147315 | -5.262558 | -0.009174 | 0.993825 | 1.276704 | 1.185175 | -1.463464 | -2.519083 | 2.754763 | 4.984591 | 1.225190 | -1.640234 | 0.435842 | 1.688162 | -1.152161 | 1.833527 | -2.404038 | -3.162171 | -0.683915 | 0.479149 | 1.570361 | -2.447488 | -0.232977 | 1.315013 | 0.676667 | -2.428220 | 3.382759 | 1.912653 | 0.272156 | -4.274340 | 1.163114 | -2.608985 | -0.675292 | 1.043775 | 2.088407 | 0.350040 | 0.292782 | 4.820057 | 0 |
| 32033 | -5.422454 | -1.158120 | 1.775486 | 1.667104 | 3.942331 | -3.745285 | -4.464691 | 2.943730 | -0.768495 | -0.158551 | -9.101413 | 1.542546 | 4.185302 | 1.198142 | -9.369858 | -3.230035 | 2.511562 | 5.799566 | -2.593738 | 1.935331 | -6.040819 | 3.116271 | 6.665841 | 8.784468 | -2.261333 | 3.797370 | -7.956913 | -2.177622 | -5.311062 | 0.956177 | 4.727832 | 14.776501 | 1.711558 | -5.538506 | 3.985035 | 5.442800 | 0.326258 | -6.648113 | 0.678211 | -2.292390 | 0 |
| 34263 | -3.782515 | -0.450532 | 0.219588 | 5.407492 | 0.921924 | -2.767813 | -2.526452 | 1.721447 | -0.366930 | -0.319374 | -5.843827 | 1.019028 | -1.696181 | -0.747773 | -3.526015 | -3.667928 | 6.197712 | 2.178782 | 3.406847 | 4.763292 | -3.168251 | 2.215267 | 3.129633 | 7.779479 | -1.045277 | -0.927424 | -7.496392 | 2.452643 | -2.014874 | 2.315269 | -1.793557 | 7.374812 | -0.354687 | 0.235708 | 3.953453 | -1.004010 | 0.178454 | -1.315403 | -0.928726 | -3.103889 | 0 |
| 22165 | 3.518076 | 1.591639 | 8.561927 | 2.207589 | -1.840818 | -3.058760 | -2.017707 | -2.420964 | 2.113670 | 0.710588 | -5.032177 | -0.252820 | 3.016377 | -1.933217 | -3.640898 | -8.866805 | -0.406599 | -0.399348 | 4.621779 | 1.300127 | -10.109142 | 1.666599 | -4.898241 | 0.900734 | 2.316946 | 4.805210 | 1.191228 | -1.759115 | -3.560131 | -2.285693 | 2.434240 | -1.118559 | -1.600219 | -0.198122 | 7.188229 | 1.951171 | -2.203954 | -2.407258 | 0.825327 | -3.448284 | 0 |
| 14701 | -1.409740 | 5.600071 | 7.200087 | 3.231392 | 2.667322 | -4.621158 | -0.364732 | 0.961820 | -0.552041 | -1.423476 | -7.881784 | 4.983002 | 1.102750 | -3.101979 | -2.701135 | -4.043730 | 4.109848 | 0.143041 | 3.346999 | 0.038593 | -5.604143 | 0.278058 | -0.684983 | 5.415044 | 0.452994 | 8.221178 | -4.765991 | -1.226450 | -4.273629 | -4.024133 | 3.909135 | 2.360448 | -3.091976 | -0.434092 | 2.475567 | 3.408487 | -0.405689 | -0.390462 | 1.611724 | -0.897909 | 0 |
| 33207 | 2.928395 | -0.666119 | 8.066194 | -0.170129 | -2.979629 | -2.985752 | -1.440611 | -1.107853 | -0.661996 | 2.797867 | -2.545840 | 3.220818 | 3.883501 | -1.451540 | -3.197608 | -6.361615 | -3.621802 | -0.669144 | 6.493697 | -0.314925 | -9.928864 | 1.932762 | -3.698124 | 1.390737 | 1.110626 | 2.974761 | 2.994170 | -2.135956 | -2.555814 | -0.032077 | 3.210467 | 0.063813 | -0.230458 | 1.386407 | 9.071553 | 3.620234 | -2.245313 | -1.727460 | 1.534836 | -4.297470 | 0 |
| 5669 | 0.935149 | -2.163003 | 0.228348 | -5.449705 | -0.383060 | 2.337819 | -0.496889 | -2.263871 | -1.080805 | 1.573563 | 1.026031 | 0.468101 | 0.451716 | 0.007627 | -0.680469 | -2.276341 | -3.471128 | 3.792456 | -2.017402 | -2.792117 | -1.427597 | 1.551873 | -0.593617 | -0.681872 | 0.555598 | -1.250410 | 3.906664 | -1.258108 | 0.262838 | 0.180364 | 1.038001 | -2.468657 | 1.632911 | -1.568220 | 0.151657 | 3.594228 | 0.663747 | 1.708052 | 1.568516 | 1.905108 | 0 |
| 38674 | -1.207808 | -0.632841 | 4.866574 | -4.251616 | -1.631265 | 2.348742 | -2.809663 | -4.549338 | 2.802451 | -1.994326 | 5.300298 | -2.162435 | 8.690677 | -1.459440 | -7.410803 | -2.931208 | -8.577440 | 1.435129 | -3.656147 | -2.373612 | -5.242921 | -2.692593 | -6.654427 | -9.686210 | -1.617298 | 5.492405 | 1.976587 | -6.278891 | 5.734939 | 4.049312 | -0.090492 | -6.192785 | 7.922213 | -3.753115 | 2.551390 | 1.354758 | 0.415164 | -2.585498 | 2.618616 | -0.103167 | 0 |
| 20842 | 1.390726 | 4.174202 | 0.070355 | -0.196379 | 0.125511 | 1.609523 | 1.333114 | -4.512807 | 1.582452 | -0.468626 | 2.943305 | 0.236747 | 2.078553 | -2.160399 | -1.558391 | -3.508716 | -3.504009 | -0.903209 | 1.357883 | -5.006154 | -2.176494 | 0.557151 | -1.291595 | -1.184702 | -1.345773 | 4.655473 | 1.904188 | -0.814605 | 0.032293 | -1.105879 | -2.595319 | -3.162378 | 1.567395 | -0.092505 | -0.329065 | -2.785143 | -1.131297 | 6.087384 | 1.039459 | -1.737122 | 1 |
| 838 | 7.888133 | 0.168398 | 2.150007 | -6.450780 | -2.280613 | 2.601208 | 0.343763 | -2.614319 | 0.546467 | -0.276030 | 3.783970 | -0.341095 | 1.384837 | -1.305235 | 0.460834 | -3.975162 | -5.162746 | 1.817028 | -1.841709 | -5.608168 | -5.689153 | 2.713931 | -1.650266 | -2.925663 | 1.635391 | 1.258803 | 5.939176 | -3.298755 | 2.230943 | 1.432734 | 2.226848 | -4.572196 | 3.018325 | -4.834265 | 4.081698 | 0.050572 | -1.017524 | 0.350919 | -1.064455 | 3.479160 | 0 |
| 1337 | -0.248381 | 4.482986 | 4.310317 | 1.438240 | 1.321043 | -2.028763 | 1.641873 | 1.888348 | -2.626784 | -0.884907 | -1.370891 | 4.678145 | -2.643515 | -3.122676 | 2.972692 | 1.687388 | 2.379856 | -2.272651 | 3.655482 | -2.056285 | -0.162754 | -1.819538 | -1.963348 | 3.385131 | -0.106559 | 4.547576 | -2.983975 | -0.174562 | 1.098094 | -0.775585 | 3.474221 | -0.325491 | -0.228824 | 0.922616 | 1.682985 | 0.476547 | -0.691523 | 2.191896 | -0.017027 | 1.446237 | 0 |
| 29416 | 3.152600 | 2.311058 | 4.118082 | 0.592644 | 0.140921 | -1.201190 | -0.652036 | -1.278103 | 0.484082 | 0.907854 | -1.438194 | -0.597727 | 2.198829 | -0.954529 | -2.253907 | -3.784931 | -2.783553 | -0.757995 | 1.711261 | -3.525908 | -6.034035 | 1.371545 | -1.037334 | 2.723498 | -0.174808 | 5.273737 | 0.542138 | -2.224999 | -2.119673 | -0.747709 | 4.279395 | 4.167341 | 2.007858 | -2.694644 | 5.377845 | 0.392590 | -2.965495 | -0.604001 | -0.507926 | -2.100223 | 0 |
| 32762 | -1.757250 | 6.391827 | 3.819737 | 1.109701 | 3.218790 | -1.263558 | -1.151295 | -2.636069 | 1.969302 | -4.001964 | -4.914844 | 2.714754 | 2.887638 | -3.945670 | -6.228100 | -7.109094 | 2.768947 | 3.591150 | -0.918753 | -1.969791 | -6.281169 | 1.578669 | 0.565396 | 2.836067 | -0.922338 | 8.927975 | -5.318660 | -2.189447 | -1.801048 | -1.851840 | -0.661659 | -0.209867 | 0.365139 | -4.097224 | 0.284495 | 0.519203 | 0.975139 | 2.052709 | 1.654416 | 0.596925 | 0 |
| 7878 | -2.392327 | 3.415965 | -3.736925 | -2.427091 | 3.078946 | 3.189773 | -0.511846 | -0.524690 | -0.998298 | -3.979600 | 4.889284 | 1.446212 | 0.871276 | -2.767772 | -4.063632 | 1.065140 | -1.676200 | 3.712325 | -4.411653 | -6.958140 | -0.549527 | 0.845265 | 4.754206 | 3.183815 | -5.167748 | 4.678583 | -6.744047 | -2.615136 | 6.298626 | 7.267616 | -0.753717 | 5.496061 | 10.033501 | -7.693856 | 1.253426 | -4.273011 | 0.542970 | 3.319531 | -1.722588 | 3.142237 | 0 |
| 26925 | 3.837595 | -2.072440 | 9.417307 | -3.680410 | -2.562387 | -4.163027 | -0.161956 | 1.205120 | -0.253021 | 1.832219 | -6.646741 | 4.534570 | 3.490407 | 0.549670 | 0.615892 | -3.501090 | -0.294935 | 0.159941 | 1.950262 | 3.978031 | -5.818237 | 0.395533 | -4.851502 | -4.590707 | 5.845809 | 0.531080 | 7.315214 | -2.357742 | -5.597554 | -7.060166 | 4.958465 | -8.479070 | -9.106476 | 3.164501 | 1.186296 | 10.097351 | 1.223169 | -6.076010 | 3.695992 | 1.074184 | 0 |
| 12607 | -2.425680 | -0.506664 | 3.514781 | -1.151009 | -0.237152 | 0.190432 | -1.288415 | -2.117050 | -0.896782 | 1.261175 | -0.127398 | 1.241002 | 1.764898 | -1.335557 | -2.672977 | -2.496119 | -2.716713 | 1.344807 | 1.619187 | -0.936929 | -2.610309 | -0.756919 | -3.381802 | -0.061361 | -0.724461 | 1.956168 | 0.318510 | -1.300969 | 0.766135 | 0.840084 | 0.849524 | -1.337842 | 2.526426 | 0.705391 | 2.258385 | 2.883784 | -0.104715 | 1.419404 | 2.382307 | -1.419505 | 0 |
| 39854 | 0.259688 | 2.440366 | -3.534673 | -2.225255 | 0.754889 | 1.845443 | 2.915781 | 2.845251 | -4.251128 | -1.951681 | 5.373184 | 5.592173 | -5.122689 | -2.744021 | 5.086834 | 5.623314 | 1.540273 | -0.287395 | 0.947797 | -4.731912 | 3.990847 | -0.174325 | 3.343122 | 3.342347 | -2.283817 | -0.896922 | -3.237748 | 0.979919 | 6.324542 | 5.167626 | -0.701658 | -0.019129 | 3.740566 | -1.005634 | 0.143507 | -3.865017 | 0.859974 | 5.230111 | -2.289807 | 4.678615 | 0 |
| 8950 | -0.953006 | -0.338357 | 3.547754 | 0.206229 | 0.394029 | -2.952504 | -0.267824 | 2.592884 | -3.382538 | 1.967266 | -4.304875 | 5.160354 | -0.857683 | -0.728523 | -0.023929 | -0.614712 | 1.489393 | 0.727254 | 3.963910 | 0.275866 | -2.938502 | 1.254206 | 1.158101 | 5.854040 | 0.190541 | 0.664465 | -1.470447 | 0.187575 | -2.569344 | -0.422813 | 3.782015 | 4.093433 | -1.569863 | 1.288420 | 3.951801 | 4.189609 | -0.378757 | -0.626282 | 0.843762 | -0.986001 | 0 |
| 2295 | -2.622058 | -3.158563 | -3.588874 | 1.726044 | 0.273727 | -1.597726 | -1.598578 | 4.773371 | -0.976914 | -1.047977 | -1.406040 | 1.161012 | 0.100366 | 1.786302 | -2.123591 | 2.850795 | 3.484825 | 1.544072 | -1.014469 | 3.997909 | 0.485587 | 1.512423 | 6.307571 | 3.389039 | -1.632488 | -3.798344 | -5.431430 | 0.882530 | 0.566629 | 4.583829 | -1.068053 | 7.770301 | 0.776861 | -1.472142 | 1.700573 | -1.127581 | 1.262651 | -5.073749 | -2.106584 | -0.363049 | 0 |
| 7832 | -3.185087 | 2.917609 | 2.775947 | 1.222887 | 2.264807 | -0.600411 | -1.942175 | -0.494578 | 0.478787 | -2.390611 | -0.813406 | 0.237111 | 3.068635 | -2.035011 | -5.360583 | -1.412303 | -0.647804 | 1.307366 | -1.358825 | -1.929435 | -3.337185 | -1.011990 | -0.373074 | 1.701388 | -2.671377 | 6.604278 | -5.699729 | -2.893938 | 1.897340 | 2.584355 | 1.904231 | 4.292168 | 5.390268 | -4.157874 | 2.620488 | -0.244187 | -0.405120 | -1.093131 | 0.272824 | -0.420296 | 0 |
| 16324 | 2.619901 | -2.413925 | 0.600497 | -8.075512 | -3.665762 | 2.076501 | 1.038067 | -2.047695 | 1.369949 | -2.329728 | 3.363480 | 4.591675 | 3.860948 | -0.814270 | -0.151087 | -2.281242 | -2.122210 | 3.152674 | -2.657355 | 1.620461 | -1.506797 | 0.871754 | -2.177629 | -11.314988 | 3.047277 | -3.412555 | 7.447356 | -1.895108 | 2.676192 | -0.607629 | -5.926502 | -17.086737 | -4.260456 | 1.606404 | -4.276949 | 3.156902 | 4.981659 | 0.046913 | 3.283668 | 4.802065 | 0 |
| 15216 | 0.477946 | 2.817104 | 3.657919 | 3.489960 | 1.071289 | -0.978437 | -1.932308 | -3.246052 | 2.451204 | -0.280825 | -4.707552 | -3.066956 | 0.182516 | -1.644659 | -3.434307 | -7.382597 | 1.791844 | 1.193017 | 1.380718 | 0.266904 | -5.339018 | 1.331696 | -2.280545 | 3.491057 | 0.615230 | 4.397930 | -2.286652 | -0.063050 | -3.041899 | -2.119227 | 0.660812 | 2.101333 | 0.236436 | -1.832504 | 2.893224 | -0.515836 | -1.722664 | 0.680496 | 0.063636 | -2.379992 | 0 |
| 7592 | -2.015061 | -2.002147 | 0.231588 | 3.281745 | -1.685410 | 1.218600 | -2.472703 | -3.323538 | 2.967489 | 0.048962 | 0.875858 | -5.720583 | 0.191335 | 0.002174 | -2.433574 | -3.571095 | 0.118902 | -0.051267 | 0.873266 | 4.053539 | -0.598454 | -1.272034 | -4.512554 | -2.682953 | -0.025846 | -1.983517 | -0.966935 | 1.190514 | 1.954594 | 2.127648 | -4.187760 | -2.278357 | 2.276399 | 1.385521 | 1.112938 | -2.879290 | -0.342473 | -0.269441 | 0.025116 | -3.005949 | 1 |
| 11342 | -3.114134 | -2.681590 | 3.042843 | 0.145222 | 0.500632 | -2.501311 | -2.428265 | 2.378354 | -1.658935 | 1.133742 | -4.509071 | 1.997590 | 1.632133 | 0.706217 | -3.313359 | -0.631462 | 0.914265 | 2.258585 | 0.508392 | 2.833581 | -2.830886 | 0.424580 | 0.805522 | 3.072703 | -0.153092 | -0.066101 | -2.588010 | -0.962076 | -1.752050 | 0.931785 | 3.240573 | 4.948075 | 0.137491 | -0.430933 | 3.170443 | 4.854230 | 0.488699 | -4.544624 | 1.119689 | -1.135798 | 0 |
| 26959 | 1.978024 | 2.099711 | 5.161170 | -8.053606 | 2.950801 | 4.463910 | -0.015479 | -7.048727 | -1.147631 | 2.997044 | -0.529525 | -3.221936 | -1.332123 | -1.609873 | 1.093530 | -4.497896 | -6.740025 | 4.559083 | -4.942969 | -9.465487 | -0.774017 | -0.865227 | -7.186459 | -0.202242 | 1.908082 | 5.808313 | 7.030390 | -3.487175 | -1.306912 | -5.987142 | 8.311981 | -4.751496 | 3.667172 | -4.312578 | -2.271209 | 7.942167 | -1.511881 | 6.062421 | 3.675792 | 4.567769 | 0 |
| 22816 | -0.963251 | -0.827803 | 3.983998 | -0.203767 | -0.333782 | -1.427901 | -1.131706 | -0.576940 | 0.149486 | 0.832869 | -3.242175 | 0.713092 | 1.492191 | -0.139672 | -1.547405 | -2.452270 | 0.060473 | 0.827557 | 0.906716 | 2.090817 | -2.374805 | -0.410999 | -2.784715 | -0.847353 | 1.412152 | 0.906214 | 0.998954 | -0.731752 | -1.931968 | -2.118602 | 1.395743 | -2.103664 | -1.980626 | 1.261398 | 0.687247 | 3.952288 | 0.413306 | -1.715105 | 2.036592 | -0.760393 | 0 |
| 18101 | -3.822218 | -6.337371 | 5.731708 | -1.426906 | -1.647300 | -4.028468 | -2.759004 | 3.829900 | -2.785537 | 3.561746 | -4.020390 | 3.799360 | 5.045523 | 2.474841 | -3.694250 | 1.656879 | -2.717142 | 0.567472 | 2.072879 | 4.807898 | -3.304932 | -1.064954 | -1.366871 | -0.666562 | 0.483808 | -1.660950 | 1.174586 | -2.287993 | -2.378434 | 0.614894 | 5.149785 | 3.372384 | -1.246181 | 2.801725 | 4.395788 | 9.120945 | 0.606261 | -8.186841 | 3.101955 | -3.056469 | 0 |
| 1916 | -5.431333 | 1.085887 | 6.811106 | 4.219266 | 1.849050 | -2.400954 | -3.356318 | -2.930456 | -0.209091 | 2.487068 | -6.536265 | -0.817603 | 1.477151 | -1.675601 | -5.787277 | -6.420179 | -0.027594 | 1.467163 | 4.085164 | 1.008827 | -5.300736 | -0.758324 | -4.517493 | 5.773476 | -1.021378 | 5.478970 | -3.867387 | -0.439399 | -3.635928 | -1.656839 | 3.165770 | 5.161141 | 1.988822 | 1.019583 | 4.552031 | 4.198152 | -1.935066 | 0.868230 | 2.993412 | -5.481389 | 0 |
| 8639 | 3.298463 | 4.682568 | 5.113761 | 3.344471 | -0.374982 | -0.820743 | -0.792388 | -4.333993 | 2.087350 | -0.325640 | -2.888046 | -1.085422 | -0.559426 | -3.686364 | -2.075880 | -9.345297 | 1.037407 | -0.079942 | 4.782536 | -1.840609 | -7.831366 | 2.176502 | -3.612464 | 3.878782 | 0.796239 | 5.464317 | -1.049268 | 0.072303 | -1.843910 | -1.303664 | -0.278585 | -0.664824 | 0.605952 | -1.046122 | 5.648077 | -2.555410 | -2.613178 | 3.675758 | -0.420055 | -2.670298 | 0 |
| 10320 | -2.169940 | -8.095259 | 1.312995 | -2.449256 | -3.333961 | -3.259636 | -2.505863 | 2.779027 | -1.092347 | 2.744347 | -4.550547 | 5.124297 | 5.144262 | 3.017121 | -4.703042 | -2.394609 | -0.159279 | 3.533835 | 2.011293 | 7.555654 | -4.674381 | 3.206664 | 2.970449 | -1.416211 | 1.615211 | -6.712158 | 2.962029 | -0.009214 | -4.296245 | 0.477268 | -2.022624 | -0.448370 | -5.646481 | 3.918126 | 2.386865 | 7.376339 | 3.013370 | -6.703391 | 3.097763 | -3.066541 | 0 |
| 26325 | 0.875170 | 3.332715 | 2.713730 | 0.206947 | -0.184475 | -0.346443 | 2.559501 | -0.794311 | -2.151212 | -0.130234 | -1.838611 | 4.869403 | -6.196734 | -3.689948 | 5.857782 | -1.858688 | 4.864932 | -0.411944 | 5.040728 | 0.028017 | 1.163293 | -0.290943 | -3.393622 | 2.200928 | 2.422794 | -0.342252 | 0.559804 | 2.968248 | 0.205088 | -3.087542 | -1.540095 | -8.480389 | -4.676878 | 4.456914 | -1.492786 | 0.522507 | 1.075513 | 6.729114 | 1.138956 | 2.577284 | 0 |
| 37359 | -4.937035 | 2.236902 | 2.536697 | 3.712979 | 2.923113 | -2.315421 | -2.251432 | 0.663396 | -1.560839 | 0.241128 | -3.860126 | 1.829740 | 1.402828 | -1.615859 | -5.581228 | -2.036824 | 0.732535 | 1.361916 | 2.350054 | -1.450472 | -4.027870 | 0.484025 | 1.994983 | 8.224701 | -3.528864 | 5.640438 | -7.514748 | -0.930746 | -1.275242 | 2.336155 | 2.989401 | 10.751336 | 4.528220 | -2.246422 | 5.063981 | 0.745043 | -1.588989 | 0.132303 | 0.296039 | -3.625554 | 0 |
| 934 | -5.063915 | -2.266599 | -2.405301 | 7.162169 | -0.852331 | -2.426153 | -3.184552 | 1.886637 | 1.762024 | -1.883306 | -3.259943 | -0.532429 | 0.203780 | 0.109652 | -5.136028 | -2.808257 | 6.603856 | 1.174160 | 3.098679 | 8.497217 | -2.130320 | 1.548362 | 3.498407 | 3.571252 | -1.772691 | -3.708653 | -8.450357 | 3.099782 | 0.303641 | 5.140608 | -7.021903 | 5.309564 | -0.100167 | 1.554870 | 3.080982 | -4.511713 | 1.351154 | -3.225144 | -1.522058 | -4.550477 | 0 |
| 1059 | -6.186177 | -8.108542 | 0.223516 | 1.256231 | -2.189138 | -3.551395 | -2.319579 | 1.739218 | -3.255670 | 6.666295 | -5.341164 | 4.873915 | 2.571051 | 3.205526 | -4.141334 | -1.960650 | -0.679749 | 2.050475 | 6.739159 | 6.548220 | -2.370278 | 2.629039 | 2.856269 | 4.693504 | -0.503629 | -7.150863 | 1.562379 | 2.849577 | -6.517947 | -0.087624 | -1.848314 | 5.385937 | -4.619579 | 7.930115 | 3.211424 | 7.219785 | 0.917178 | -1.614542 | 4.017853 | -8.032529 | 0 |
| 39741 | 4.874024 | 4.662761 | 3.992354 | -4.321318 | -0.314187 | 5.037405 | 0.759302 | -10.237813 | 1.158098 | 1.505763 | 2.951474 | -2.432460 | -1.818189 | -4.731006 | 0.418109 | -10.327949 | -5.670874 | 2.583103 | 1.506693 | -9.222988 | -5.173965 | 1.591611 | -7.708568 | -0.110276 | 1.085443 | 5.345976 | 6.325880 | -1.140727 | 0.940797 | -2.545688 | -0.149062 | -9.028782 | 4.143074 | -1.900438 | 1.797460 | -0.421055 | -2.341939 | 11.519712 | 2.035732 | 0.983373 | 0 |
| 15329 | -4.912595 | 0.139576 | 4.519411 | 6.289688 | 0.688782 | -5.931440 | 0.140678 | 5.310164 | -4.076335 | 2.903048 | -5.469728 | 5.308271 | -1.513486 | -0.003745 | 1.390108 | 3.967325 | 3.547396 | -4.412256 | 8.062408 | 3.907455 | 0.622792 | -2.365646 | -0.358325 | 6.809396 | -0.823681 | 1.022177 | -4.709051 | 2.002178 | -3.216741 | -1.121286 | 4.224187 | 7.301938 | -3.197789 | 6.212681 | 3.695445 | 3.161115 | -1.452096 | -1.948200 | 0.892213 | -4.898129 | 0 |
| 33320 | -2.729167 | 1.787046 | 0.901403 | 1.990250 | 0.883145 | -0.939270 | 1.746310 | 3.000418 | -5.416413 | 2.094295 | 2.896547 | 4.535503 | -3.615455 | -1.844912 | 3.679290 | 6.092982 | -1.058997 | -3.816754 | 5.793825 | -4.014072 | 2.815575 | -2.487400 | -0.191000 | 6.282417 | -3.291519 | 1.679281 | -3.650542 | 0.896179 | 3.314297 | 3.704295 | 3.794634 | 6.154224 | 4.581883 | 2.453345 | 4.043425 | -1.189945 | -2.302315 | 4.027575 | -0.866137 | -1.491120 | 0 |
| 20868 | 3.967242 | 3.811891 | 0.905339 | -1.483613 | 0.644813 | 2.375611 | 0.660628 | -4.223658 | 1.090814 | -0.815709 | 0.571743 | -1.583514 | -2.296678 | -2.694680 | 0.859424 | -5.387943 | -0.362021 | 1.706271 | -0.375303 | -4.825939 | -2.752404 | 1.858892 | -1.758603 | 1.628742 | 0.806350 | 3.122854 | 1.341710 | -0.247782 | 0.321589 | -1.243018 | -0.021253 | -2.973993 | 1.649759 | -3.051104 | 0.753465 | -2.021721 | -1.150883 | 5.292691 | -0.641361 | 2.047750 | 0 |
| 37485 | -0.214805 | 0.668438 | -2.889039 | -2.402379 | 0.649493 | -0.642055 | 1.704629 | 5.009142 | -2.849296 | -2.861005 | 1.656963 | 6.762354 | -0.778379 | -0.789173 | 1.560024 | 5.114493 | 2.619503 | 0.901953 | -1.116321 | -0.907780 | 1.692708 | 0.982391 | 6.112030 | 1.424116 | -1.347590 | -1.076197 | -3.305274 | -0.343666 | 2.991123 | 3.537254 | -0.618067 | 1.114488 | -0.046066 | -1.601697 | -0.658196 | -0.848772 | 2.433715 | -0.825444 | -1.422182 | 4.141998 | 0 |
| 27646 | -1.769310 | -4.066963 | 0.779931 | -2.754965 | -1.603481 | 1.079969 | 0.316275 | 0.424806 | -3.588525 | 3.250432 | 1.568847 | 1.884174 | -3.362150 | 0.221899 | 3.883074 | 2.485683 | -1.160271 | 0.419053 | 2.329486 | 0.944300 | 3.239089 | -1.557832 | -3.286024 | -0.359926 | 0.893350 | -5.196322 | 2.973269 | 1.479054 | 2.123892 | 1.081692 | 0.828156 | -3.771898 | 0.112301 | 4.156641 | 0.079938 | 3.617931 | 0.765109 | 1.928082 | 1.597640 | 0.929518 | 0 |
| 2736 | -1.086983 | 5.353976 | 2.601932 | -3.068120 | 4.373146 | 0.529368 | -2.313409 | -1.818596 | 1.163432 | -4.368318 | -1.262307 | 1.419037 | 6.450320 | -2.864208 | -9.411345 | -4.452145 | -2.973607 | 5.419023 | -6.170474 | -7.309580 | -7.610867 | 1.967319 | 3.403508 | 2.762868 | -3.586247 | 11.465052 | -5.745607 | -6.368209 | 0.925003 | 2.406367 | 3.954885 | 6.579599 | 7.836170 | -10.841393 | 2.862046 | 1.112552 | -0.054341 | -1.037981 | 0.399726 | 2.267109 | 0 |
| 12765 | 4.181065 | 5.224632 | -0.737006 | 0.570363 | 0.125711 | 2.705133 | 1.019096 | -5.021785 | 0.867339 | -0.600750 | 3.166156 | -0.889556 | -2.195091 | -3.833727 | -0.225930 | -6.363403 | -1.565668 | 0.440086 | 2.842350 | -7.118870 | -4.420012 | 3.070561 | -0.064495 | 4.482095 | -1.591006 | 4.035244 | -0.401762 | 0.458487 | 1.495866 | 1.872101 | -2.272976 | 0.403480 | 4.657582 | -2.913625 | 4.026654 | -6.314181 | -2.680747 | 8.491854 | -1.876409 | -0.845833 | 0 |
| 21524 | -2.747635 | -6.594639 | -1.643412 | -0.422898 | -3.937079 | -2.510679 | 0.531061 | 4.127044 | -2.848615 | 2.428872 | -0.639752 | 6.982953 | 0.054516 | 1.918643 | 1.650035 | 2.737596 | 2.295183 | -0.342680 | 5.375385 | 7.643786 | 1.680468 | 0.914560 | 2.322348 | -1.580037 | 1.136625 | -9.541539 | 2.260051 | 3.280300 | -0.909051 | 1.434541 | -5.249257 | -4.128114 | -6.727150 | 8.474752 | -0.291297 | 3.051585 | 3.296155 | -1.862505 | 1.969147 | -2.225212 | 0 |
| 19784 | 0.282338 | -1.085178 | 3.670420 | -0.068380 | -0.129647 | -2.223873 | -1.405949 | 1.333491 | -0.269893 | 0.722833 | -3.569085 | 0.776765 | 1.962521 | 0.532484 | -1.952127 | -1.414230 | -0.025751 | 0.635581 | 0.368756 | 1.506101 | -3.526296 | 0.510562 | -0.423888 | 0.982196 | 0.961196 | 1.247673 | -0.085327 | -1.453232 | -2.246375 | -0.891784 | 3.490177 | 2.387629 | -1.009538 | -0.791548 | 2.822253 | 3.566278 | -0.365300 | -4.195742 | 0.548485 | -0.711159 | 0 |
| 28183 | -3.443435 | 0.731864 | 2.361046 | 0.552655 | 2.201074 | -1.241716 | -2.205199 | -1.147175 | -0.166345 | -0.023378 | -5.572138 | 1.451899 | 1.231707 | -1.087385 | -4.966986 | -4.988857 | 1.860367 | 4.232781 | -0.125455 | 0.560041 | -3.958589 | 1.716236 | 1.051631 | 4.622073 | -0.562105 | 2.915026 | -3.476992 | -0.522232 | -3.047166 | -1.037072 | 0.845798 | 3.431796 | 0.102309 | -1.650910 | 1.170807 | 3.536618 | 0.683360 | 0.216288 | 1.931433 | -1.058035 | 0 |
| 4502 | -0.107253 | 6.869189 | 2.834417 | -1.870937 | 5.573679 | -0.197953 | -1.880910 | 0.126224 | 0.010710 | -4.990121 | -3.035746 | 1.743563 | 2.964403 | -3.545752 | -6.978909 | -3.444529 | 0.300987 | 5.253891 | -5.892843 | -7.666627 | -6.944507 | 2.231397 | 4.532120 | 6.695260 | -3.171824 | 11.876629 | -8.384915 | -5.543338 | 0.957015 | 2.404709 | 6.314163 | 9.530007 | 7.689339 | -12.222339 | 3.769880 | 0.335678 | -0.626627 | -1.218607 | -1.413825 | 4.094527 | 0 |
| 36599 | -2.249221 | 4.902632 | 3.514602 | -0.335406 | 1.646639 | 0.340608 | -1.083515 | -3.657662 | 3.958151 | -5.429191 | -1.094918 | 0.350505 | 4.831033 | -3.236377 | -5.687233 | -4.746537 | 0.854351 | 2.277870 | -3.616473 | 0.071210 | -3.274337 | -1.530859 | -3.287490 | -5.678799 | -0.176925 | 7.469694 | -2.753674 | -3.184414 | 1.675879 | -1.454356 | -3.175468 | -7.828906 | 0.561856 | -2.891486 | -3.293885 | -0.055038 | 2.488599 | 0.343278 | 2.529787 | 2.041560 | 0 |
| 10774 | -3.518492 | -2.928104 | -1.792213 | -2.694210 | 1.195120 | 0.905239 | -2.585371 | 0.274408 | -0.866964 | -0.220061 | -0.834426 | 0.746244 | 2.313111 | 0.675523 | -5.302342 | -1.215882 | -0.949989 | 5.400909 | -3.455608 | -0.094306 | -1.866860 | 1.964585 | 3.843151 | 2.206419 | -2.023004 | -0.937362 | -2.761915 | -1.421180 | 0.621946 | 3.695819 | -0.099572 | 4.904142 | 3.841495 | -3.683959 | 1.129938 | 2.462408 | 1.579258 | -1.846542 | 0.722062 | 0.490174 | 0 |
| 28593 | 3.743450 | 0.493146 | -0.110931 | 0.133180 | -1.655480 | -0.748047 | 0.184454 | 0.031818 | 1.323478 | -0.598825 | 0.776768 | 0.780041 | 2.456463 | -0.062618 | -1.529920 | -2.378029 | -1.085964 | -0.720370 | 1.279518 | -0.641349 | -4.304188 | 2.536377 | 2.102516 | -0.344211 | 0.043663 | 0.601036 | 1.175034 | -0.759694 | -0.855785 | 0.972367 | -1.292537 | 0.733427 | -0.499580 | -1.121414 | 3.110320 | -2.280386 | -0.780529 | -1.189953 | -1.266482 | -1.399364 | 0 |
| 39117 | 8.997744 | 5.473639 | 7.210307 | -6.096252 | 2.624195 | 0.765112 | 1.651765 | -4.227535 | 2.454247 | 0.191613 | -4.539364 | -3.676397 | 1.057763 | -0.326677 | 2.712783 | -4.674614 | -3.530092 | 0.920481 | -6.419837 | -7.325433 | -3.325436 | 0.245984 | -5.381479 | -3.365860 | 5.558976 | 8.904073 | 9.060213 | -4.727108 | -6.338563 | -12.103603 | 10.433273 | -6.130529 | -4.314374 | -5.787277 | -3.717637 | 7.097485 | -2.027434 | -0.387890 | 1.661222 | 5.866787 | 0 |
| 5104 | -2.412642 | -5.558407 | -2.103972 | -5.916636 | 0.196852 | 1.656959 | -3.247044 | 0.577889 | -1.187475 | 0.274617 | 0.258510 | 0.885066 | 3.922066 | 1.695910 | -6.217389 | -1.158431 | -3.201273 | 7.100451 | -5.335342 | -0.379981 | -3.017695 | 2.944748 | 4.613991 | 0.505553 | -1.703670 | -2.722386 | -0.581392 | -2.738622 | 1.230896 | 5.071782 | 0.640116 | 4.579502 | 4.801586 | -5.132473 | 2.020343 | 4.254343 | 2.150092 | -3.983696 | 0.864653 | 1.549631 | 0 |
| 38687 | 1.656341 | 3.618063 | 5.772809 | 5.241011 | -1.760381 | -1.161691 | -1.882363 | -5.102476 | 2.647897 | 0.027209 | -1.883801 | -1.421938 | 0.925941 | -4.009650 | -4.231777 | -10.599511 | 0.172967 | -0.840176 | 7.228748 | -0.093322 | -9.391124 | 1.739872 | -5.043812 | 3.166940 | -0.211672 | 4.994217 | -2.036640 | 0.319383 | -0.642280 | 1.066932 | -2.565547 | -0.634941 | 2.180272 | 0.586273 | 8.031740 | -3.936065 | -2.916311 | 3.515892 | -0.099053 | -5.598333 | 0 |
| 8283 | 2.096736 | 1.620796 | 7.416156 | 5.865169 | -2.924312 | -4.669526 | -1.793909 | -2.244810 | 3.859154 | -0.014073 | -6.314673 | 0.487174 | 3.885491 | -1.654238 | -4.452919 | -9.861103 | 2.497223 | -1.975391 | 7.233062 | 5.802178 | -9.825874 | 1.803292 | -4.233427 | -0.488723 | 2.600657 | 3.145058 | 0.281511 | 0.351949 | -5.194998 | -3.333047 | -2.730555 | -3.406664 | -5.801331 | 3.690723 | 5.568824 | -0.104899 | -1.165022 | -2.444072 | 1.316987 | -6.266475 | 0 |
| 13891 | -2.095674 | 2.684565 | 2.290561 | -3.914793 | 2.452809 | 2.163160 | -2.790761 | -5.466081 | 2.561138 | -2.895036 | -1.066343 | -0.303466 | 6.045884 | -2.534376 | -9.290841 | -7.891579 | -3.327248 | 6.770604 | -5.332506 | -4.253228 | -6.738003 | 1.859616 | -0.277535 | -1.126555 | -1.808131 | 7.406561 | -1.737507 | -4.543851 | 0.371191 | 0.636635 | -0.713736 | -1.229507 | 5.196304 | -6.856038 | 0.127428 | 2.334711 | 1.381358 | 1.517235 | 2.796993 | 1.226590 | 0 |
| 23582 | -2.194218 | 6.837869 | 5.270404 | 2.723597 | 4.621822 | -4.987882 | -1.809178 | 1.232026 | 1.395936 | -4.308945 | -9.597337 | 5.300099 | 5.509161 | -2.797791 | -8.784380 | -6.209616 | 4.472576 | 3.474888 | -0.962826 | -0.744909 | -8.986904 | 2.859483 | 4.865812 | 6.383896 | -1.291295 | 11.389374 | -8.209541 | -3.415807 | -5.674908 | -2.965418 | 2.942295 | 7.157960 | -1.757294 | -5.677398 | 2.216711 | 2.890083 | 0.671764 | -3.068978 | 1.244305 | -0.613644 | 0 |
| 25107 | 5.000746 | 3.184763 | 5.772400 | -3.541984 | -0.616347 | 1.810300 | 0.503589 | -6.279666 | 1.018308 | 1.911503 | 0.961322 | -1.326003 | 1.766392 | -2.355110 | -0.447999 | -6.735988 | -6.338451 | 0.195733 | 1.134553 | -6.843468 | -5.708063 | 0.675987 | -6.306155 | -1.614202 | 1.501555 | 6.007468 | 6.648762 | -2.804137 | -1.425072 | -3.773153 | 3.457358 | -5.387322 | 1.694318 | -1.495085 | 2.597829 | 2.281355 | -2.644321 | 4.747644 | 2.016646 | -0.283485 | 0 |
| 11254 | 2.520429 | 2.821725 | 4.011747 | -2.300072 | 1.436268 | -0.180450 | 0.858380 | -0.833358 | -1.699033 | 1.128427 | -1.595617 | 1.486701 | -0.954974 | -1.504850 | 1.481705 | -1.331047 | -1.753356 | 0.327948 | 0.332502 | -5.009813 | -2.222080 | 0.277521 | -1.758707 | 2.842020 | 0.825128 | 4.543437 | 1.768456 | -1.734572 | -1.560601 | -2.819755 | 5.903302 | 0.655870 | 0.573978 | -1.990830 | 1.668273 | 3.207943 | -1.641436 | 1.947549 | 0.598494 | 1.719890 | 0 |
| 19867 | -0.659597 | 0.124732 | 2.524773 | 4.615359 | -1.538344 | -2.939798 | -0.639984 | 3.253937 | -3.232861 | 0.294420 | -2.335526 | 4.788576 | -4.877731 | -2.956167 | 1.465892 | -1.693389 | 5.713598 | -0.754457 | 8.475319 | 3.022756 | -3.623367 | 1.528472 | 0.590021 | 8.288855 | -0.574211 | -1.986289 | -6.241360 | 2.961911 | 1.935200 | 5.164838 | -0.761928 | 4.105166 | 0.745913 | 2.426956 | 8.241338 | -2.685479 | -0.815883 | 0.848968 | -2.259555 | -1.920635 | 1 |
| 19643 | 0.162076 | 2.367033 | -1.120161 | 2.583352 | -0.212926 | -0.230492 | 1.295653 | -1.737330 | 0.627955 | 1.113843 | 2.056345 | 0.316206 | 1.703743 | -0.334743 | -0.842052 | -0.548592 | -2.618907 | -3.088820 | 3.427347 | -2.628564 | -0.616053 | 0.251589 | 0.634083 | 0.895601 | -1.927733 | 2.416235 | 0.750205 | 0.580148 | -1.444223 | -0.632107 | -1.957826 | 1.852859 | 0.371911 | 2.137551 | 0.703696 | -3.094630 | -1.940970 | 3.853116 | 0.207777 | -4.386565 | 1 |
| 11564 | 0.448173 | 10.610164 | -3.176369 | 2.049803 | 3.533792 | 0.979242 | 5.760755 | 4.118832 | -5.578625 | -4.741158 | 6.807657 | 8.290273 | -8.751518 | -6.552521 | 8.336770 | 8.576485 | 4.487996 | -4.435238 | 4.016738 | -9.691431 | 5.743757 | -1.949024 | 4.037167 | 8.410141 | -4.710063 | 5.922040 | -9.269515 | 1.582516 | 8.764035 | 5.710779 | 0.584230 | 3.097917 | 6.055640 | -1.924810 | 0.777698 | -9.658997 | -1.086230 | 10.487004 | -4.640493 | 5.515833 | 0 |
| 18884 | -1.268408 | -2.496197 | 0.627246 | -6.739367 | 1.468796 | 1.983353 | -4.041787 | -1.471827 | 1.561666 | -2.442874 | -0.386654 | -1.186868 | 7.191095 | 0.622584 | -9.262507 | -3.712232 | -4.360516 | 7.921116 | -9.056324 | -2.146250 | -5.801643 | 2.258152 | 2.605493 | -2.614795 | -1.391043 | 2.998326 | -0.983365 | -5.876652 | 1.446433 | 3.530822 | 2.140198 | 2.602506 | 6.224466 | -9.257485 | 1.276738 | 4.491911 | 2.106292 | -5.641362 | 1.284730 | 3.178896 | 0 |
| 20037 | 4.803092 | -1.646071 | 8.060060 | -2.999106 | -2.296365 | -2.816431 | -1.189136 | -0.095199 | 0.816151 | 2.321447 | -2.736627 | 0.509172 | 6.529515 | 1.273307 | -2.630432 | -3.183255 | -5.700051 | -1.082184 | 0.646405 | -0.401828 | -7.765497 | 0.564291 | -3.827210 | -3.845292 | 2.790210 | 3.608657 | 6.561973 | -4.568099 | -4.068059 | -3.498807 | 6.813221 | -1.099164 | -2.121730 | -0.850582 | 5.038778 | 6.652885 | -1.808880 | -6.744977 | 1.827548 | -1.668879 | 0 |
| 92 | -0.138117 | 2.530108 | 7.933042 | -0.414104 | 2.367527 | -5.183271 | -2.848292 | 1.081957 | 1.609267 | -2.433871 | -11.271652 | 4.354174 | 6.293131 | -1.125199 | -8.068847 | -7.776729 | 3.308159 | 4.827209 | -1.790524 | 2.297128 | -10.428752 | 3.191712 | 1.933821 | 2.698730 | 1.992713 | 7.752338 | -3.041910 | -4.035741 | -7.132044 | -4.843732 | 4.568291 | 2.570757 | -4.752658 | -4.522678 | 2.545789 | 7.698981 | 1.475007 | -6.897804 | 2.557333 | 0.401122 | 0 |
| 14828 | 2.871708 | -0.044733 | 3.334780 | 7.415387 | -0.368238 | -0.682196 | -3.422553 | -2.880027 | 2.462968 | 2.148597 | -5.972942 | -9.516021 | -6.233716 | -0.579007 | 0.339560 | -8.589289 | 4.839996 | 0.091067 | 3.858455 | 3.394306 | -4.715148 | 1.797990 | -4.435351 | 8.006570 | 2.289845 | -0.781120 | -3.255812 | 3.237778 | -2.768303 | -0.321299 | 2.216339 | 6.693015 | 1.475429 | -1.512772 | 7.348206 | -3.511944 | -4.466254 | -0.729811 | -3.568725 | -3.639102 | 1 |
| 7687 | -4.309183 | 0.719820 | -1.548007 | 1.714867 | 1.717995 | -1.426710 | -0.780586 | 1.869102 | -1.897431 | -0.590501 | -1.568128 | 4.308503 | 0.886771 | -0.926253 | -3.779522 | 0.009382 | 1.743353 | 1.891939 | 1.669512 | -0.299642 | -1.514575 | 1.620869 | 5.148818 | 5.912766 | -3.240629 | 1.284958 | -5.901142 | 0.386067 | -0.266652 | 3.198945 | -1.235098 | 6.995454 | 1.993413 | -0.674513 | 2.067512 | -0.444890 | 0.633367 | 1.005916 | 0.117720 | -1.932465 | 0 |
| 38658 | -2.539709 | 3.727880 | 1.393137 | -1.179106 | 4.335508 | -0.538226 | -2.611371 | -0.821011 | 1.388345 | -3.954148 | -4.617195 | 1.266520 | 4.418762 | -1.825996 | -8.480464 | -4.899903 | 1.184499 | 6.120765 | -5.484369 | -2.787677 | -5.899888 | 2.500809 | 4.396179 | 3.859030 | -2.183470 | 7.853470 | -6.476684 | -3.820812 | -1.436239 | 0.594230 | 1.888497 | 6.060286 | 3.545221 | -8.282187 | 0.719052 | 1.921014 | 1.204088 | -1.827210 | 0.681992 | 1.835883 | 0 |
| 33911 | -5.540539 | 2.629961 | 3.199335 | 3.396468 | 3.777230 | -1.430969 | -2.380136 | -1.424276 | -1.098826 | 0.981376 | -5.087239 | -0.027867 | 0.286045 | -1.702922 | -5.085727 | -3.521111 | 0.760454 | 2.231406 | 1.472162 | -1.785678 | -2.982008 | -0.113399 | -0.196360 | 8.038768 | -2.659252 | 6.091745 | -6.301198 | -0.527382 | -2.449643 | -0.228193 | 3.482548 | 8.830201 | 3.834726 | -1.858597 | 2.963097 | 2.248443 | -1.624445 | 1.932114 | 1.444999 | -3.235696 | 0 |
| 15351 | -5.159928 | -1.476738 | 0.795043 | 1.289906 | 1.419583 | -1.015171 | -1.442906 | 0.028581 | -3.053397 | 3.377435 | -2.681996 | 2.117833 | -0.379638 | -0.027613 | -2.620109 | -0.843667 | -0.742980 | 1.729965 | 3.245314 | -0.524457 | -0.856560 | 0.509169 | 1.071685 | 6.767631 | -2.279490 | 0.188189 | -2.653513 | 0.925377 | -2.074407 | 1.023704 | 1.816525 | 7.137663 | 2.143126 | 1.688384 | 2.791239 | 3.257649 | -0.848184 | 2.169487 | 1.799861 | -3.767929 | 0 |
| 3931 | -4.817769 | -0.511750 | 1.191105 | 3.711206 | 0.692439 | -1.703229 | -1.766119 | -0.382143 | -0.871705 | 1.477701 | -3.499612 | 1.159999 | -0.115646 | -0.657800 | -3.262967 | -2.700614 | 1.863299 | 1.009438 | 3.869654 | 2.408501 | -1.788877 | 0.386029 | 0.134924 | 4.982417 | -1.512331 | 0.270317 | -3.995129 | 1.597902 | -1.837359 | 0.837274 | -1.190470 | 4.381036 | 0.420253 | 2.334970 | 2.427960 | 0.898016 | -0.266761 | 1.261535 | 1.314184 | -4.205206 | 0 |
| 9161 | -0.222342 | -0.900145 | -0.517244 | 1.348841 | -1.523793 | 1.315455 | -2.234680 | -2.983418 | 2.196829 | -0.902203 | 1.131867 | -2.500734 | 1.008419 | -1.163926 | -4.021860 | -5.524449 | -0.118071 | 2.064186 | 0.925957 | 1.315648 | -4.078095 | 1.761493 | -0.738534 | 0.012469 | -0.923432 | -0.837452 | -1.791359 | 0.313677 | 2.021928 | 4.004873 | -4.532896 | -0.339757 | 3.424480 | -1.314726 | 3.486752 | -3.526080 | -0.091592 | 0.786848 | -0.682631 | -2.078918 | 0 |
| 38683 | -1.010717 | 1.095024 | 3.249547 | -0.497145 | 3.155540 | -3.155011 | -1.543759 | 4.255707 | -2.555290 | -0.910594 | -6.602478 | 3.762890 | 0.040005 | -0.509047 | -2.445691 | -0.164777 | 3.331344 | 3.335216 | -1.561922 | -0.594193 | -3.791621 | 1.827967 | 4.257393 | 7.427629 | -0.286435 | 3.766404 | -5.495176 | -2.013532 | -2.514673 | 0.098370 | 6.940118 | 9.023364 | 0.455973 | -4.960758 | 3.468731 | 4.632701 | 0.116341 | -4.639891 | -0.624249 | 2.237379 | 0 |
| 20871 | 2.730671 | 3.093048 | 1.836231 | 3.838548 | -1.401105 | 0.037822 | -1.155681 | -5.397836 | 4.101245 | -0.639326 | -1.085259 | -2.816724 | 1.682499 | -2.319903 | -4.101534 | -9.815357 | 0.043962 | -0.007368 | 3.790628 | -0.206755 | -7.285601 | 2.842440 | -2.064948 | 1.027957 | 0.129568 | 3.261164 | -0.131349 | 0.604149 | -1.950813 | -0.458539 | -4.612988 | -1.496804 | 0.259119 | -0.297326 | 4.010451 | -4.669497 | -1.910437 | 3.354441 | -0.340102 | -4.494401 | 0 |
| 32534 | 0.049207 | 0.829131 | 5.298914 | 2.016929 | -1.838601 | -0.983080 | -1.363678 | -5.000668 | 1.767355 | 2.744454 | -0.648062 | -0.762515 | 4.752603 | -1.228845 | -4.823503 | -7.105502 | -5.100761 | -1.435643 | 5.391665 | -1.089511 | -6.939265 | 0.382198 | -5.091340 | -0.648615 | -0.463592 | 4.284296 | 2.945975 | -1.200611 | -2.752890 | -1.458387 | -0.585309 | -1.132797 | 0.958088 | 2.626779 | 4.893515 | 0.934236 | -2.562101 | 2.303779 | 2.694402 | -6.784859 | 0 |
| 17820 | -5.701904 | -4.193348 | 6.274914 | 4.640738 | -1.189976 | -4.878756 | -4.453672 | 1.915281 | 0.877926 | 0.952567 | -7.449006 | 0.081463 | 3.798826 | 1.084835 | -5.939363 | -3.111456 | 3.025690 | 0.659444 | 3.117748 | 9.771678 | -4.513395 | -1.354093 | -3.265820 | -0.055219 | 1.003513 | -0.708416 | -3.806745 | -0.197287 | -2.825853 | 0.119661 | 0.563804 | 2.214263 | -2.567392 | 3.266470 | 3.916445 | 5.105324 | 0.931969 | -8.186096 | 2.381588 | -4.858921 | 0 |
| 10944 | -4.498237 | -1.722403 | 3.792182 | 6.222314 | -1.907715 | -2.913939 | -3.705945 | -0.360810 | 0.412206 | 0.436365 | -3.594438 | 0.832771 | 0.941654 | -1.850445 | -5.588727 | -5.990486 | 3.162818 | 0.600703 | 7.037652 | 6.258624 | -6.272828 | 0.726263 | -2.083426 | 4.129131 | -1.300859 | -0.586036 | -6.164964 | 1.627515 | 0.419176 | 4.669369 | -3.666508 | 3.248125 | 1.723549 | 2.858374 | 7.641784 | -1.428502 | -0.383692 | -1.421973 | 0.368598 | -6.193342 | 0 |
| 37525 | 0.128468 | 1.003224 | 1.245051 | -1.226191 | -0.629781 | 2.660156 | -0.744762 | -5.408680 | -0.727781 | 2.259530 | 3.138366 | -0.352528 | 0.228497 | -2.620150 | -2.608993 | -5.717648 | -5.159645 | 1.649442 | 3.345125 | -5.705018 | -4.425443 | 1.568024 | -2.586864 | 3.032134 | -2.201984 | 2.424895 | 1.215730 | -0.500335 | 1.647644 | 2.855421 | -0.875322 | 0.525500 | 6.295429 | -0.708402 | 4.974246 | -1.318620 | -2.207511 | 7.038809 | 0.970871 | -2.889489 | 0 |
| 14910 | -1.180926 | -0.827852 | -0.171873 | 2.071741 | 0.101280 | -2.888468 | -0.448933 | 3.826284 | -1.994821 | 0.161975 | -3.370998 | 4.123947 | -0.778593 | 0.050033 | -0.764102 | 0.325275 | 3.696860 | 0.574800 | 2.958252 | 2.490534 | -1.882801 | 2.075707 | 4.441705 | 5.498543 | -0.599925 | -1.474626 | -3.870250 | 1.270178 | -1.671876 | 1.730008 | 0.188477 | 5.711134 | -1.702832 | 0.680756 | 3.149977 | 0.417520 | 0.382956 | -2.038247 | -0.934557 | -1.267524 | 0 |
| 26374 | 1.197857 | -0.184383 | -1.026144 | -6.037185 | 0.198955 | 0.544702 | 0.145614 | 0.751908 | 0.238695 | -3.655476 | -1.396262 | 5.370318 | 2.117456 | -0.947729 | -2.114865 | -2.345025 | 2.227495 | 5.811418 | -4.581504 | 0.409682 | -2.498710 | 3.256410 | 4.266340 | -2.622759 | 1.404489 | -0.819217 | 0.611713 | -1.700961 | 0.235958 | -0.065679 | -2.800447 | -6.171423 | -3.060390 | -3.219623 | -3.047530 | 2.902653 | 4.541561 | -1.229140 | 1.238382 | 5.703920 | 0 |
| 10728 | -0.995933 | -1.084175 | -4.410008 | 2.567126 | 0.889016 | -0.791154 | 0.063196 | 1.200845 | -0.199765 | 3.303184 | 2.190349 | -2.141156 | 4.316176 | 3.715427 | -3.141635 | 3.775099 | -5.807663 | -3.216178 | 0.423148 | -3.371568 | 0.628244 | 0.873332 | 5.755187 | 3.300899 | -4.073982 | 1.186996 | 0.146182 | -0.577394 | -3.514907 | 0.973382 | 2.044177 | 13.307747 | 3.047088 | -0.444443 | 1.925967 | -1.890811 | -3.487816 | -1.176273 | -1.100995 | -6.643971 | 1 |
| 11600 | -2.311630 | -1.373151 | 7.611768 | -1.332194 | 0.462576 | -0.736360 | -2.959389 | -4.068984 | -0.456981 | 4.269938 | -3.993338 | -1.467130 | 3.794596 | -0.178639 | -5.009151 | -5.792871 | -5.577533 | 2.076645 | 1.449712 | -1.635491 | -5.705016 | -0.656619 | -6.152315 | 1.216015 | 0.327180 | 4.547391 | 2.844612 | -2.790995 | -3.671849 | -2.863476 | 5.846799 | 1.653282 | 2.302932 | 0.102682 | 4.028287 | 7.758824 | -2.049224 | -0.320608 | 4.150245 | -3.820435 | 0 |
| 22467 | -2.320551 | -0.450545 | 2.616592 | -2.064503 | 1.282256 | -0.056561 | -2.947654 | -1.552641 | 0.703966 | -0.970594 | -2.755221 | 0.327590 | 4.063561 | -0.727840 | -6.663208 | -4.565675 | -1.313871 | 4.834307 | -2.838042 | -0.452035 | -5.202865 | 1.363655 | 0.334298 | 0.743953 | -0.885923 | 3.360867 | -2.130244 | -2.938175 | -0.532355 | 1.195415 | 1.281444 | 2.078702 | 2.994744 | -4.167125 | 2.040458 | 3.526692 | 0.939660 | -2.208514 | 1.732636 | 0.141674 | 0 |
| 6715 | -2.062043 | -1.124779 | 8.646586 | 3.687433 | -1.555939 | -4.069064 | -3.389656 | -1.348103 | 0.692562 | 1.982006 | -6.912350 | 0.944885 | 2.909221 | -1.267539 | -5.134236 | -7.653757 | 0.973826 | 0.525014 | 6.008105 | 5.056258 | -8.299659 | 0.346514 | -5.224063 | 2.081158 | 1.444091 | 2.456556 | -1.219461 | -0.491070 | -3.716963 | -1.310109 | 1.418468 | 0.322523 | -1.871568 | 2.853337 | 6.643105 | 4.441649 | -0.959064 | -3.123217 | 2.597503 | -5.368614 | 0 |
| 39944 | 3.009295 | 1.768554 | 2.932902 | 2.051275 | -1.207896 | -0.352412 | -1.616565 | -1.995221 | 2.234696 | -0.998938 | 0.437929 | -2.372558 | 2.142491 | -1.641343 | -3.407901 | -5.152674 | -1.463246 | -0.562804 | 1.766646 | -1.229737 | -6.682681 | 1.382800 | -1.677218 | 0.822347 | -0.547738 | 3.693288 | -1.402614 | -1.647654 | 0.950235 | 2.596099 | 0.236708 | 2.196002 | 3.746528 | -3.101098 | 6.372071 | -3.430689 | -2.335036 | -1.121291 | -1.733269 | -2.264665 | 0 |
# check number of rows and columns
data.shape
(40000, 41)
#check for missing data
data.isna().sum().sort_values(ascending=False)
V1 46 V2 39 V40 0 V30 0 V23 0 V24 0 V25 0 V26 0 V27 0 V28 0 V29 0 V31 0 V39 0 V22 0 V33 0 V34 0 V35 0 V36 0 V37 0 V38 0 V32 0 V21 0 V20 0 V10 0 V3 0 V4 0 V5 0 V6 0 V7 0 V8 0 V9 0 V11 0 V19 0 V12 0 V13 0 V14 0 V15 0 V16 0 V17 0 V18 0 Target 0 dtype: int64
#check for missing data
data_test.isna().sum().sort_values(ascending=False)
V1 11 V2 7 V40 0 V30 0 V23 0 V24 0 V25 0 V26 0 V27 0 V28 0 V29 0 V31 0 V39 0 V22 0 V33 0 V34 0 V35 0 V36 0 V37 0 V38 0 V32 0 V21 0 V20 0 V10 0 V3 0 V4 0 V5 0 V6 0 V7 0 V8 0 V9 0 V11 0 V19 0 V12 0 V13 0 V14 0 V15 0 V16 0 V17 0 V18 0 Target 0 dtype: int64
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 40000 entries, 0 to 39999 Data columns (total 41 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 V1 39954 non-null float64 1 V2 39961 non-null float64 2 V3 40000 non-null float64 3 V4 40000 non-null float64 4 V5 40000 non-null float64 5 V6 40000 non-null float64 6 V7 40000 non-null float64 7 V8 40000 non-null float64 8 V9 40000 non-null float64 9 V10 40000 non-null float64 10 V11 40000 non-null float64 11 V12 40000 non-null float64 12 V13 40000 non-null float64 13 V14 40000 non-null float64 14 V15 40000 non-null float64 15 V16 40000 non-null float64 16 V17 40000 non-null float64 17 V18 40000 non-null float64 18 V19 40000 non-null float64 19 V20 40000 non-null float64 20 V21 40000 non-null float64 21 V22 40000 non-null float64 22 V23 40000 non-null float64 23 V24 40000 non-null float64 24 V25 40000 non-null float64 25 V26 40000 non-null float64 26 V27 40000 non-null float64 27 V28 40000 non-null float64 28 V29 40000 non-null float64 29 V30 40000 non-null float64 30 V31 40000 non-null float64 31 V32 40000 non-null float64 32 V33 40000 non-null float64 33 V34 40000 non-null float64 34 V35 40000 non-null float64 35 V36 40000 non-null float64 36 V37 40000 non-null float64 37 V38 40000 non-null float64 38 V39 40000 non-null float64 39 V40 40000 non-null float64 40 Target 40000 non-null int64 dtypes: float64(40), int64(1) memory usage: 12.5 MB
data[data.duplicated()].count()
V1 0 V2 0 V3 0 V4 0 V5 0 V6 0 V7 0 V8 0 V9 0 V10 0 V11 0 V12 0 V13 0 V14 0 V15 0 V16 0 V17 0 V18 0 V19 0 V20 0 V21 0 V22 0 V23 0 V24 0 V25 0 V26 0 V27 0 V28 0 V29 0 V30 0 V31 0 V32 0 V33 0 V34 0 V35 0 V36 0 V37 0 V38 0 V39 0 V40 0 Target 0 dtype: int64
# Let's look at the statistical summary of the data
data.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| V1 | 39954.0 | -0.288120 | 3.449072 | -13.501880 | -2.751460 | -0.773518 | 1.836708 | 17.436981 |
| V2 | 39961.0 | 0.442672 | 3.139431 | -13.212051 | -1.638355 | 0.463939 | 2.537508 | 13.089269 |
| V3 | 40000.0 | 2.505514 | 3.406263 | -11.469369 | 0.202682 | 2.265319 | 4.584920 | 18.366477 |
| V4 | 40000.0 | -0.066078 | 3.437330 | -16.015417 | -2.349574 | -0.123691 | 2.148596 | 13.279712 |
| V5 | 40000.0 | -0.044574 | 2.107183 | -8.612973 | -1.507206 | -0.096824 | 1.346224 | 9.403469 |
| V6 | 40000.0 | -1.000849 | 2.036756 | -10.227147 | -2.363446 | -1.006635 | 0.373909 | 7.065470 |
| V7 | 40000.0 | -0.892793 | 1.756510 | -8.205806 | -2.036913 | -0.934738 | 0.206820 | 8.006091 |
| V8 | 40000.0 | -0.563123 | 3.298916 | -15.657561 | -2.660415 | -0.384188 | 1.714383 | 11.679495 |
| V9 | 40000.0 | -0.007739 | 2.161833 | -8.596313 | -1.493676 | -0.052085 | 1.425713 | 8.507138 |
| V10 | 40000.0 | -0.001848 | 2.183034 | -11.000790 | -1.390549 | 0.105779 | 1.486105 | 8.108472 |
| V11 | 40000.0 | -1.917794 | 3.116426 | -14.832058 | -3.940969 | -1.941726 | 0.089444 | 13.851834 |
| V12 | 40000.0 | 1.578095 | 2.914613 | -13.619304 | -0.431373 | 1.485367 | 3.540787 | 15.753586 |
| V13 | 40000.0 | 1.591309 | 2.865222 | -13.830128 | -0.208522 | 1.653836 | 3.476336 | 15.419616 |
| V14 | 40000.0 | -0.946620 | 1.787759 | -8.309443 | -2.164513 | -0.957444 | 0.265874 | 6.213289 |
| V15 | 40000.0 | -2.435720 | 3.341244 | -17.201998 | -4.451365 | -2.398608 | -0.381757 | 12.874679 |
| V16 | 40000.0 | -2.943168 | 4.211646 | -21.918711 | -5.631812 | -2.718600 | -0.112947 | 13.583212 |
| V17 | 40000.0 | -0.142794 | 3.344332 | -17.633947 | -2.227048 | -0.027895 | 2.071801 | 17.404510 |
| V18 | 40000.0 | 1.188949 | 2.586164 | -11.643994 | -0.402848 | 0.867433 | 2.564239 | 13.179863 |
| V19 | 40000.0 | 1.181333 | 3.394979 | -13.491784 | -1.050903 | 1.278402 | 3.497277 | 16.059004 |
| V20 | 40000.0 | 0.027201 | 3.674985 | -13.922659 | -2.433811 | 0.030136 | 2.513245 | 16.052339 |
| V21 | 40000.0 | -3.621359 | 3.556979 | -19.436404 | -5.920847 | -3.559327 | -1.284178 | 13.840473 |
| V22 | 40000.0 | 0.943242 | 1.645538 | -10.122095 | -0.112147 | 0.962802 | 2.018031 | 7.409856 |
| V23 | 40000.0 | -0.387617 | 4.052147 | -16.187510 | -3.118868 | -0.275339 | 2.438047 | 15.080172 |
| V24 | 40000.0 | 1.142220 | 3.912820 | -18.487811 | -1.483210 | 0.963586 | 3.563055 | 19.769376 |
| V25 | 40000.0 | -0.003019 | 2.024691 | -8.228266 | -1.373400 | 0.021100 | 1.399816 | 8.223389 |
| V26 | 40000.0 | 1.895717 | 3.421454 | -12.587902 | -0.319231 | 1.963826 | 4.163146 | 16.836410 |
| V27 | 40000.0 | -0.616838 | 4.392161 | -14.904939 | -3.692075 | -0.909640 | 2.200608 | 21.594552 |
| V28 | 40000.0 | -0.888121 | 1.924947 | -9.685082 | -2.192763 | -0.904757 | 0.376856 | 6.906865 |
| V29 | 40000.0 | -1.005327 | 2.676299 | -12.579469 | -2.799008 | -1.206027 | 0.604473 | 11.852476 |
| V30 | 40000.0 | -0.032664 | 3.031009 | -14.796047 | -1.908202 | 0.184613 | 2.040131 | 13.190889 |
| V31 | 40000.0 | 0.505885 | 3.482735 | -19.376732 | -1.798975 | 0.491352 | 2.777519 | 17.255090 |
| V32 | 40000.0 | 0.326831 | 5.499369 | -23.200866 | -3.392115 | 0.056243 | 3.789241 | 24.847833 |
| V33 | 40000.0 | 0.056542 | 3.574219 | -17.454014 | -2.237550 | -0.049729 | 2.255985 | 16.692486 |
| V34 | 40000.0 | -0.464127 | 3.185712 | -17.985094 | -2.127757 | -0.250842 | 1.432885 | 14.358213 |
| V35 | 40000.0 | 2.234861 | 2.924185 | -15.349803 | 0.332081 | 2.110125 | 4.044659 | 16.804859 |
| V36 | 40000.0 | 1.530020 | 3.819754 | -17.478949 | -0.937119 | 1.571511 | 3.996721 | 19.329576 |
| V37 | 40000.0 | -0.000498 | 1.778273 | -7.639952 | -1.265717 | -0.132620 | 1.160828 | 7.803278 |
| V38 | 40000.0 | -0.351199 | 3.964186 | -17.375002 | -3.016805 | -0.318724 | 2.291342 | 15.964053 |
| V39 | 40000.0 | 0.900035 | 1.751022 | -7.135788 | -0.261578 | 0.921321 | 2.069016 | 7.997832 |
| V40 | 40000.0 | -0.897166 | 2.997750 | -11.930259 | -2.949590 | -0.949269 | 1.092178 | 10.654265 |
| Target | 40000.0 | 0.054675 | 0.227348 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
# function to plot a boxplot and a histogram along the same scale.
# import the library for labelling
import matplotlib.patheffects as path_effects
# import the library for labelling
import matplotlib.patheffects as path_effects
def add_median_labels(ax):
lines = ax.get_lines()
# determine number of lines per box (this varies with/without fliers)
boxes = [c for c in ax.get_children() if type(c).__name__ == 'PathPatch']
lines_per_box = int(len(lines) / len(boxes))
# iterate over median lines
for median in lines[4:len(lines):lines_per_box]:
# display median value at center of median line
x, y = (data.mean() for data in median.get_data())
# choose value depending on horizontal or vertical plot orientation
value = x if (median.get_xdata()[1]-median.get_xdata()[0]) == 0 else y
text = ax.text(x, y, f'{value:.1f}', ha='center', va='center',
fontweight='bold', color='white', bbox=dict(facecolor='black'),size=15)
# create median-colored border around white text for contrast
text.set_path_effects([
path_effects.Stroke(linewidth=3, foreground=median.get_color()),
path_effects.Normal(),
])
def box_and_histogram(column, figsize=(10,10), bins = None):
""" Boxplot and histogram together, with median labels on boxplot
df_series: dataframe column
figsize: size of fig (default (9,8))
bins: number of bins (default None / auto)
color of mean is green and median is black
"""
f2, (ax_box2, ax_hist2) = plt.subplots(nrows = 2, # Number of rows of the subplot grid= 2
sharex = True, # x-axis will be shared among all subplots
gridspec_kw = {"height_ratios": (.25, .75)},
figsize = figsize
) # creating the 2 subplots
box_plot = sns.boxplot(column, ax=ax_box2,showmeans=True, color='red')
add_median_labels(box_plot.axes)
sns.distplot(column, kde=F, bins=bins) if bins else sns.distplot(column, kde=True, ) # For histogram
ax_hist2.axvline(np.mean(column), color='g', linestyle='--') # Add mean to the histogram
ax_hist2.axvline(np.median(column), color='black', linestyle='-') # Add median to the histogram
# Let's visualize the data for variables 1 to 5
columns = ['V1','V2','V3','V4','V5' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 6 to 10
columns = ['V6','V7','V8','V9','V10' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 11 to 15
columns = ['V11','V12','V13','V14','V15' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 16 to 20
columns = ['V16','V17','V18','V19','V20' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 21 to 25
columns = ['V21','V22','V23','V24','V25' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 26 to 30
columns = ['V26','V27','V28','V29','V30' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 31 to 35
columns = ['V31','V32','V33','V34','V35' ]
for col in columns:
box_and_histogram(data[col])
# Let's visualize the data for variables 36 to 40
columns = ['V36','V37','V38','V39','V40' ]
for col in columns:
box_and_histogram(data[col])
cols=data.loc[:,data.columns!="Target"].columns
cols
Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',
'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',
'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31',
'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V40'],
dtype='object')
plt.figure(figsize=(20, 40))
for i, variable in enumerate(cols):
plt.subplot(8, 5, i + 1)
sns.boxplot(data["Target"], data[variable])
plt.tight_layout()
plt.title(variable)
plt.show()
plt.figure(figsize=(20, 40))
for i, variable in enumerate(cols):
plt.subplot(8, 5, i + 1)
sns.lineplot(y=data["Target"], x=data[variable] ,hue=data['Target'], ci=None)
plt.tight_layout()
plt.title(variable)
plt.show()
plt.figure(figsize=(25, 20))
sns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()
missing_data = data[data.isnull().any(axis=1)]
missing_data
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | V29 | V30 | V31 | V32 | V33 | V34 | V35 | V36 | V37 | V38 | V39 | V40 | Target | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | NaN | -3.961403 | 2.787804 | -4.712526 | -3.007329 | -1.541245 | -0.881148 | 1.476656 | 0.574700 | -1.100884 | -1.847500 | 4.540851 | 4.489899 | 0.709830 | -2.137588 | -2.025606 | 0.135720 | 2.791922 | -1.166851 | 4.870138 | -3.923793 | 1.492618 | -0.173071 | -6.470502 | 3.008428 | -3.133608 | 3.956271 | -1.898191 | -0.642024 | -0.537602 | -1.875548 | -8.326069 | -5.140552 | 1.121314 | -0.305907 | 5.315007 | 3.750044 | -5.631174 | 2.372485 | 2.195956 | 0 |
| 273 | 8.340257 | NaN | 4.701014 | -6.210458 | -3.452383 | 1.470407 | 1.363145 | -3.737388 | 2.563769 | 0.580317 | 5.224782 | -1.100279 | 6.722174 | 0.108789 | -0.147561 | -2.254103 | -9.812704 | -2.557279 | -1.523474 | -5.423774 | -5.215212 | -0.193827 | -5.445650 | -10.165518 | 2.283469 | 4.110351 | 10.904055 | -5.266057 | 0.344776 | -2.668169 | 2.833545 | -9.074177 | 0.302138 | -1.629747 | 1.621417 | 1.755205 | -1.708377 | -1.411025 | 1.290293 | 0.752315 | 0 |
| 1245 | -2.048681 | NaN | -1.623885 | -3.324224 | 0.152256 | 0.600157 | -1.812802 | 0.852194 | -1.522600 | 0.211071 | -0.459977 | 2.379694 | 1.676241 | 0.529289 | -3.767787 | -1.095793 | -0.785049 | 4.855301 | -1.960680 | 0.046844 | -2.195123 | 2.567045 | 3.987672 | 2.067719 | -1.311846 | -2.227351 | -1.315420 | -0.934344 | 0.535158 | 3.590032 | -0.471473 | 3.264218 | 2.379064 | -2.457084 | 1.719365 | 2.537010 | 1.701780 | -1.434535 | 0.597365 | 0.739238 | 0 |
| 1380 | -5.418573 | NaN | -3.572559 | 5.968870 | -0.205538 | -1.109785 | -0.932011 | 3.405683 | -1.115739 | 0.522278 | -0.237106 | -1.564628 | -4.009767 | 1.379675 | 1.645199 | 4.632539 | 4.977622 | -2.015693 | 2.710159 | 6.348299 | 5.534624 | -2.107946 | 1.175364 | 2.906464 | -1.503223 | -5.756979 | -5.847501 | 4.122799 | 1.977548 | 3.739087 | -3.099835 | 4.859093 | 0.336079 | 3.993229 | -0.140748 | -3.344090 | 0.364732 | -1.402871 | -1.717696 | -2.597330 | 1 |
| 1912 | NaN | -4.396035 | 2.966584 | 0.295102 | 0.731237 | -1.994700 | -3.058456 | 1.751438 | -0.829251 | 1.678691 | -3.877690 | -0.523262 | 3.117887 | 2.224083 | -3.885601 | 1.687679 | -0.578267 | 1.532526 | -1.483454 | 4.780401 | 0.522266 | -2.495555 | -1.826535 | -0.986531 | -0.206230 | -0.713276 | -1.621114 | -1.308794 | -1.380073 | -0.199121 | 3.110573 | 3.066807 | 0.169947 | 1.215512 | -0.164512 | 6.554772 | 1.116389 | -5.987598 | 2.751352 | -1.781519 | 0 |
| 2592 | -4.319088 | NaN | 2.843283 | 2.945775 | 3.595187 | 0.281369 | -0.896436 | -3.177737 | -2.770721 | 1.762175 | -1.028361 | 1.305554 | -1.669878 | -3.953334 | -3.296903 | -3.628615 | -1.775791 | 0.984566 | 4.450028 | -7.065080 | -3.236503 | 0.027602 | -1.015812 | 10.481567 | -4.444116 | 7.823805 | -6.029542 | -0.253706 | 0.202040 | 2.016165 | 3.365177 | 9.047712 | 7.782101 | -1.792136 | 5.452533 | -0.715179 | -3.376629 | 7.879568 | 0.763107 | -3.694618 | 0 |
| 4520 | -3.760658 | NaN | 0.194954 | -1.637958 | 1.261479 | -1.573947 | -3.685700 | 1.575651 | -0.309823 | -0.137656 | -4.495353 | 1.816832 | 5.029424 | 1.437108 | -8.108689 | -2.802929 | -0.186850 | 5.801223 | -3.025006 | 2.018854 | -5.082972 | 3.032960 | 5.197027 | 3.117241 | -1.580460 | 0.259425 | -3.535269 | -2.269980 | -2.474176 | 2.470193 | 1.162112 | 7.620821 | 1.695061 | -3.956354 | 2.707644 | 4.657387 | 1.619307 | -5.537285 | 1.246650 | -1.162793 | 0 |
| 4535 | NaN | -2.290514 | 5.281270 | 0.061313 | -2.685212 | -2.957848 | -1.926706 | 0.586094 | 0.588523 | 1.390606 | -2.393746 | 1.518457 | 4.980041 | 0.476893 | -3.904766 | -3.755668 | -2.423004 | -0.395431 | 3.052598 | 2.336750 | -7.451835 | 1.437825 | -1.472597 | -1.057866 | 1.040043 | 0.968170 | 1.961925 | -2.177968 | -2.257775 | 0.458950 | 1.933139 | 0.912012 | -1.003690 | 0.486942 | 6.282024 | 3.008357 | -0.929962 | -5.314752 | 0.854975 | -3.397366 | 0 |
| 4993 | NaN | -4.064771 | 2.664544 | 0.558965 | -2.042545 | -2.631083 | -2.490838 | 2.443172 | -1.374747 | 1.323602 | -2.383205 | 2.849271 | 2.432142 | 0.510173 | -3.615126 | -1.671053 | 0.131941 | 1.302810 | 3.201164 | 3.966235 | -4.931732 | 1.503287 | 0.929219 | 2.002754 | -0.300393 | -2.239765 | -1.686891 | -0.487229 | -0.385455 | 3.638871 | 0.487030 | 3.687370 | 0.453439 | 0.926712 | 6.125896 | 2.568266 | 0.309537 | -4.769939 | 0.352315 | -2.787734 | 0 |
| 5071 | -1.430888 | NaN | 0.659576 | -2.876402 | 1.150137 | -0.785760 | -1.560174 | 2.898635 | -2.346989 | -0.217607 | -1.131122 | 2.931200 | 2.053438 | 0.375193 | -3.122665 | 1.321348 | -1.052983 | 3.187626 | -2.288253 | -1.314396 | -2.461230 | 1.291901 | 3.693753 | 3.002806 | -1.522996 | 0.903917 | -2.649923 | -2.502205 | 0.678484 | 3.294856 | 3.915422 | 6.279266 | 3.323914 | -4.047760 | 3.119220 | 3.336260 | 0.603524 | -3.781725 | -0.157478 | 1.503298 | 0 |
| 5653 | NaN | -3.665339 | -2.198754 | -2.519645 | -2.675553 | 0.164510 | -0.402929 | 0.212563 | 1.702956 | 0.336125 | 0.074108 | -0.454947 | 2.318662 | 2.219356 | -1.027244 | -2.392829 | -0.795781 | 1.643923 | -1.377537 | 2.245399 | -2.768113 | 3.580877 | 3.026772 | -2.842542 | 1.882535 | -4.602956 | 4.345017 | -0.041658 | -2.037299 | 0.014794 | -2.573927 | -1.815817 | -3.195830 | -0.512145 | 0.487590 | 0.244933 | 0.990139 | -3.344451 | -0.621139 | -0.005606 | 1 |
| 6994 | -0.478281 | NaN | 9.976495 | 4.031651 | -2.760167 | -6.158360 | -3.228606 | 1.690966 | 1.073824 | 0.872343 | -8.008404 | 2.204678 | 4.029947 | -0.419443 | -4.259935 | -5.785115 | 2.628383 | -1.007896 | 5.987351 | 8.195792 | -8.818298 | -0.026899 | -4.856576 | -0.316481 | 2.938750 | 1.504419 | -0.991382 | -1.044576 | -3.950048 | -1.634292 | 2.156943 | -0.894560 | -4.845863 | 3.355665 | 7.026692 | 5.048423 | -0.240258 | -7.993494 | 1.877513 | -4.483384 | 0 |
| 7978 | NaN | -1.075159 | 1.469704 | 4.483204 | -0.050699 | -4.700674 | -0.618391 | 2.984803 | -1.962131 | 1.617979 | -6.776674 | 5.878601 | -0.355491 | -0.041995 | -1.702440 | -1.704497 | 5.360431 | 0.198781 | 6.430233 | 5.763087 | -2.027481 | 1.673272 | 2.932935 | 6.022883 | 0.002866 | -1.878647 | -3.692318 | 3.011419 | -4.934942 | -1.316966 | -1.909387 | 3.643091 | -5.862770 | 5.478892 | 1.812677 | 2.702366 | 1.068124 | -0.568559 | 1.668301 | -4.411335 | 0 |
| 8127 | 2.925940 | NaN | -2.645660 | -3.375497 | -0.833483 | -0.084212 | -0.959669 | 3.381121 | -0.176873 | -0.651842 | 0.755793 | 0.199790 | 2.485080 | 2.491418 | -1.675918 | 1.745380 | -1.240703 | 2.170497 | -4.025295 | 0.111117 | -2.002820 | 2.894916 | 5.746456 | -0.281012 | -0.086922 | -2.799500 | 0.556632 | -1.939904 | 0.189052 | 3.222765 | 1.697374 | 4.958088 | 1.149066 | -4.575331 | 2.203397 | 0.546033 | 0.590221 | -6.276125 | -2.211247 | 1.937313 | 0 |
| 8851 | -1.675059 | NaN | 5.880634 | -0.825969 | 1.132624 | -2.863011 | -2.736460 | 1.792533 | 0.078862 | -0.823493 | -7.186496 | 0.938892 | 1.953796 | 0.157937 | -3.207803 | -2.455756 | 2.800190 | 3.290769 | -2.254053 | 4.040877 | -3.763496 | -0.199837 | -1.576407 | 0.292080 | 2.254909 | 1.992280 | -1.956794 | -2.217381 | -2.533441 | -2.207347 | 4.601579 | 0.451657 | -2.293099 | -2.106819 | 1.063332 | 6.826214 | 1.502499 | -6.729947 | 1.690577 | 1.840973 | 0 |
| 9290 | NaN | 3.816469 | -0.286405 | 2.531866 | 2.027175 | 1.014996 | -0.319793 | -2.481382 | 2.251727 | -2.139864 | -1.375899 | -3.586545 | -2.112576 | -1.621963 | -0.439420 | -3.101997 | 2.806070 | 0.714796 | -1.512009 | -0.989120 | -0.076803 | 0.008082 | -0.896183 | 1.778960 | -0.020348 | 3.160956 | -3.058581 | 0.614823 | -0.007178 | -1.226789 | -0.918743 | 0.260917 | 1.085035 | -2.589996 | -1.318923 | -3.202846 | -0.633049 | 2.419982 | -1.093407 | 0.867432 | 0 |
| 9420 | 5.465769 | NaN | 4.540947 | -2.916550 | 0.399752 | 2.798925 | 0.029477 | -7.334071 | 1.122874 | 1.695269 | 1.164994 | -2.778245 | 0.571042 | -3.077622 | -1.388269 | -8.513207 | -6.207813 | 1.401325 | 0.768958 | -9.145467 | -6.873133 | 2.065480 | -4.811998 | 1.896627 | 0.337629 | 7.159794 | 4.652823 | -2.619055 | -1.106923 | -2.283696 | 3.652232 | -1.535753 | 4.596212 | -4.103525 | 4.295524 | 0.152672 | -3.726700 | 6.562692 | 0.706452 | -0.461696 | 0 |
| 10137 | NaN | -1.368619 | -0.546050 | 1.089182 | 2.367816 | -0.077836 | -1.947327 | -0.441766 | -2.473149 | 2.185105 | -4.466692 | 0.725899 | -3.031391 | -0.370541 | -2.098992 | -2.549900 | 2.600584 | 4.334367 | 1.166349 | 0.573882 | -0.118077 | 1.529874 | 1.906148 | 8.143036 | -1.329583 | -1.225747 | -4.151775 | 2.032476 | -2.091154 | 0.671457 | 0.729161 | 6.396190 | 1.473689 | 0.061643 | 1.120271 | 2.856940 | 0.225613 | 2.679514 | 1.144952 | -1.394502 | 0 |
| 11992 | NaN | 1.008391 | 1.227702 | 5.397082 | 0.064230 | -2.706919 | -2.028368 | 0.534046 | 3.006797 | -2.362238 | -5.713246 | -1.619682 | -0.046163 | -0.511042 | -3.029988 | -4.996103 | 6.425079 | 0.772994 | 1.235166 | 5.859541 | -3.850601 | 1.707097 | 1.015553 | 2.309973 | 1.161525 | 0.387843 | -4.908116 | 1.452770 | -2.538740 | -0.518046 | -2.748630 | 1.869502 | -3.115298 | -0.550197 | 1.713781 | -2.256960 | 0.410992 | -3.434400 | -1.299388 | -1.768734 | 0 |
| 12210 | NaN | 3.316925 | 5.060659 | -0.446360 | -3.532064 | 1.529328 | -0.260512 | -7.110460 | 4.353728 | -0.919140 | 2.269669 | -2.500927 | 2.593713 | -3.509146 | -2.069539 | -10.587414 | -3.614693 | -0.640328 | 3.286654 | -2.714384 | -9.050848 | 2.048937 | -6.631660 | -4.396718 | 2.003188 | 4.191047 | 5.049098 | -1.669602 | 0.766574 | -0.647296 | -3.214030 | -9.320682 | 1.016370 | -0.940099 | 4.953367 | -3.652571 | -2.006196 | 3.604607 | 0.145745 | -1.694669 | 0 |
| 12245 | NaN | 4.493707 | 7.642149 | 9.793549 | -0.909513 | -3.181438 | -4.222176 | -5.573967 | 4.423335 | -0.267999 | -6.637891 | -3.796125 | 1.507684 | -4.287660 | -8.072626 | -15.188750 | 2.981120 | -0.090454 | 8.605178 | 2.407955 | -13.533658 | 3.066518 | -4.646861 | 7.365895 | -0.431990 | 7.158626 | -6.216343 | 0.752885 | -3.423318 | 0.911583 | -2.274335 | 4.982505 | 2.342984 | -0.802676 | 11.371154 | -4.849225 | -4.178397 | 1.402169 | -0.863317 | -8.729919 | 0 |
| 12766 | NaN | -5.205346 | 1.997652 | -3.707913 | -1.042200 | -1.593126 | -2.653309 | 0.852280 | -1.310489 | 2.406924 | -2.696178 | 3.516630 | 6.079544 | 1.892509 | -6.296278 | -2.354264 | -3.712552 | 4.058640 | -0.373002 | 1.624398 | -5.273210 | 2.432719 | 2.354406 | 0.062126 | -0.468669 | -1.307987 | 1.865487 | -2.446031 | -2.908037 | 1.165988 | 1.492476 | 3.074149 | -0.067649 | -0.277521 | 3.196840 | 7.016205 | 1.302334 | -4.580096 | 2.956254 | -2.363150 | 0 |
| 13017 | NaN | -0.868840 | 1.814902 | -6.639786 | 0.936485 | 1.885892 | 0.515150 | -1.033423 | -2.260966 | 1.915017 | 0.550571 | 0.957892 | -0.017215 | 0.247460 | 1.458545 | 0.629232 | -4.302598 | 2.625095 | -3.305733 | -4.945039 | 0.227560 | 0.057033 | -1.363722 | -0.639107 | 1.046686 | 1.012381 | 4.812950 | -2.453318 | -0.310829 | -2.057215 | 5.528878 | -1.750476 | 1.285502 | -2.347532 | -1.014564 | 5.857372 | 0.036564 | 0.992831 | 1.747799 | 3.714604 | 0 |
| 13052 | NaN | 2.146202 | 5.004415 | 4.192063 | 1.427887 | -6.438263 | -0.931339 | 3.794120 | -0.683032 | -0.738941 | -8.188528 | 6.675745 | 4.109352 | -0.652655 | -4.763335 | -1.714802 | 4.042106 | -0.464425 | 4.026334 | 3.829667 | -5.309701 | 0.925650 | 2.933306 | 4.457374 | -0.354467 | 4.864187 | -5.042871 | -0.770122 | -5.668711 | -2.644126 | 1.854951 | 5.231472 | -5.113312 | 1.745687 | 2.587189 | 3.990777 | 0.610716 | -4.273457 | 1.864568 | -3.599079 | 0 |
| 13744 | -2.631454 | NaN | 2.330188 | 1.090080 | 0.603973 | -1.139383 | -0.690121 | -1.358935 | 0.355568 | -1.189176 | -1.703057 | 3.140829 | 2.523075 | -2.170738 | -3.983002 | -3.456933 | 0.497438 | 1.160180 | 1.968360 | 0.019241 | -3.499027 | 0.380632 | -0.338494 | 0.911370 | -1.197353 | 3.693568 | -2.561312 | -0.728583 | -0.449598 | 0.165491 | -1.960479 | -0.950215 | 0.209717 | 0.448728 | 1.046063 | 0.536937 | 0.763187 | 1.728621 | 1.885821 | -1.701774 | 0 |
| 14270 | NaN | 2.534010 | 2.762821 | -1.673718 | -1.942214 | -0.029961 | 0.911323 | -3.199743 | 2.948610 | -0.413229 | 0.013372 | -0.482538 | 2.908428 | -0.941512 | -0.654617 | -6.153308 | -2.604109 | -0.673503 | 0.766564 | -2.703691 | -6.404446 | 2.858015 | -1.414298 | -2.859224 | 2.361779 | 3.167914 | 5.590179 | -1.768976 | -2.734201 | -3.303544 | -0.201166 | -4.887077 | -2.611526 | -1.500807 | 2.036186 | -0.828979 | -1.369591 | 0.572366 | -0.132183 | -0.322007 | 0 |
| 15225 | -3.282076 | NaN | 1.089303 | -0.297256 | -0.847120 | -2.071730 | -1.018769 | 1.903559 | -1.992877 | 1.303162 | -1.057456 | 4.867177 | 3.254604 | 0.322403 | -3.446038 | 0.209071 | -1.157670 | 0.877536 | 2.771877 | 1.694064 | -2.538209 | 0.797491 | 2.024747 | 1.381218 | -1.510307 | -0.787676 | -1.093965 | -0.544585 | -0.760790 | 2.220833 | -0.460547 | 2.646007 | -0.033489 | 2.065799 | 2.818009 | 2.942201 | 0.906275 | -1.654521 | 1.776612 | -2.816281 | 0 |
| 15405 | NaN | 5.810825 | 6.117779 | 4.272288 | 2.745355 | -4.587927 | 0.940203 | 4.796476 | -3.072551 | -1.537984 | -3.592306 | 5.020504 | -1.499441 | -2.888753 | 1.553317 | 3.751712 | 3.462779 | -3.899402 | 3.964719 | -1.673536 | -1.190407 | -2.715009 | -0.633227 | 6.187782 | -1.244325 | 7.828489 | -7.061031 | -1.303818 | 0.343013 | -0.193841 | 7.171046 | 6.391124 | 0.872783 | -0.693302 | 4.089487 | 0.536159 | -1.912093 | -1.558981 | -1.129738 | 0.314055 | 0 |
| 15657 | 3.496481 | NaN | 1.791834 | -3.741859 | -0.229485 | 2.297874 | 0.554526 | -3.809588 | 1.130483 | -0.616944 | 0.028601 | -0.925496 | -1.345047 | -1.708126 | 1.430691 | -4.505205 | -0.531143 | 2.434766 | -1.814582 | -2.383142 | -1.570527 | 0.951037 | -3.309759 | -2.362724 | 2.502444 | 0.962072 | 3.994129 | -0.653084 | 0.119796 | -2.765007 | -0.192701 | -7.776764 | -1.000756 | -1.505009 | -1.694084 | 1.277005 | 0.616155 | 3.204568 | 0.980778 | 3.523759 | 0 |
| 15756 | -4.203459 | NaN | 2.953868 | 0.584466 | 4.103940 | -0.639211 | -2.810799 | -0.112492 | -1.362768 | -0.800101 | -1.392398 | 0.420056 | 3.812381 | -1.782387 | -7.548892 | -1.170379 | -3.183952 | 2.584919 | -1.855763 | -5.778520 | -4.961650 | -0.045195 | 1.937032 | 6.761591 | -4.828079 | 9.170575 | -7.402776 | -4.275830 | 0.949874 | 3.958575 | 6.185166 | 12.522374 | 9.502488 | -7.152953 | 5.668769 | 1.249833 | -2.158520 | -0.954461 | -0.002385 | -1.546808 | 0 |
| 16634 | NaN | -2.183497 | -2.832760 | 0.856029 | 0.394697 | -1.234254 | -0.837151 | 3.985010 | -0.742953 | -1.159604 | -1.146809 | 1.191437 | -0.040457 | 1.460338 | -0.974668 | 2.892405 | 3.006014 | 1.096985 | -1.442688 | 3.116455 | 1.127400 | 0.877835 | 4.877453 | 1.847520 | -0.877461 | -2.898755 | -3.800126 | 0.582652 | 0.383981 | 2.802459 | -0.652246 | 4.921478 | -0.220851 | -1.100686 | 0.176513 | -0.486785 | 1.360086 | -4.091073 | -1.498897 | 0.589119 | 0 |
| 16711 | -0.362145 | NaN | 5.105150 | -4.258467 | -0.282131 | -1.687061 | -3.105052 | 0.087244 | 1.960376 | -1.727349 | -5.522109 | 1.128942 | 6.030070 | 0.783059 | -5.925117 | -4.543553 | 0.099486 | 5.200058 | -4.853106 | 4.137746 | -5.815107 | 1.098229 | -1.105992 | -4.886038 | 2.782986 | 1.270419 | 1.488477 | -3.914160 | -2.424635 | -2.174155 | 1.837435 | -4.272084 | -2.957263 | -3.053080 | -0.326437 | 7.707428 | 3.110962 | -7.844149 | 2.950426 | 2.481726 | 0 |
| 17059 | NaN | -1.398710 | -2.008106 | -1.750341 | 0.932279 | -1.290327 | -0.270476 | 4.458834 | -2.776270 | -1.211766 | -2.048816 | 5.283325 | -0.871558 | 0.067832 | -0.667495 | 1.865210 | 3.443319 | 3.297417 | -0.930368 | 0.944236 | -0.557582 | 2.546979 | 6.470965 | 4.467335 | -0.810854 | -2.224774 | -3.843865 | 0.170131 | 0.231628 | 2.963375 | 0.415482 | 4.560244 | -0.420834 | -2.037313 | 1.109793 | 1.520594 | 2.113872 | -2.252571 | -0.939249 | 2.542411 | 0 |
| 17076 | NaN | -3.840585 | 0.197220 | 4.147789 | 1.151400 | -0.993298 | -4.732363 | 0.558966 | -0.926683 | 0.457914 | -4.888741 | -1.246722 | -1.652650 | -0.234745 | -5.406765 | -2.988888 | 4.833866 | 4.638228 | 1.297109 | 6.398805 | -1.092482 | 0.134456 | 0.410457 | 6.207151 | -1.939004 | -2.995823 | -8.530204 | 2.124160 | 0.821271 | 4.870719 | -2.012722 | 6.818725 | 3.451213 | 0.241818 | 3.215765 | 1.203210 | 1.274857 | -1.921229 | 0.578890 | -2.837521 | 0 |
| 17164 | -4.484232 | NaN | 1.200644 | -2.042064 | 2.779443 | -0.801748 | -5.403548 | -1.225314 | 1.485831 | -0.974382 | -5.912861 | -0.328605 | 7.564604 | 0.804895 | -12.687494 | -7.008813 | -1.560600 | 8.507858 | -5.537049 | 0.199815 | -8.388053 | 4.008961 | 5.066444 | 3.765416 | -2.404518 | 4.072617 | -4.742182 | -4.099999 | -3.458606 | 2.146155 | 1.661795 | 9.467401 | 4.281421 | -7.588117 | 3.266825 | 5.232311 | 1.278590 | -5.370513 | 1.984130 | -1.643391 | 0 |
| 17825 | NaN | 1.807829 | 0.754077 | -3.492142 | 1.535349 | -0.264430 | -0.828235 | -0.332537 | 0.650625 | -3.180095 | -2.407683 | 4.117280 | 3.779339 | -1.545960 | -5.061719 | -3.814858 | 0.762752 | 5.008758 | -3.465819 | -1.424448 | -4.728830 | 2.830022 | 3.674099 | 0.130095 | -0.380836 | 3.579698 | -1.806614 | -2.686608 | -0.806861 | 0.061592 | -0.827272 | -1.010136 | -0.217304 | -4.430743 | -0.537355 | 2.375030 | 2.541389 | -0.820695 | 1.347275 | 2.746619 | 0 |
| 17954 | 3.263555 | NaN | 8.446574 | -3.253218 | -3.417978 | -2.995838 | -0.669271 | -0.161283 | -0.666870 | 3.133527 | -2.111679 | 3.734586 | 5.746368 | 0.330360 | -1.831358 | -3.276834 | -5.364977 | -1.125289 | 3.783334 | 0.578734 | -7.445814 | 0.402815 | -4.710375 | -3.815327 | 2.681128 | 1.784631 | 7.026010 | -3.364239 | -3.217049 | -2.714911 | 4.554619 | -4.242730 | -3.122680 | 2.522415 | 5.283805 | 7.291310 | -0.867555 | -4.315230 | 3.124488 | -2.393239 | 0 |
| 18040 | -3.793170 | NaN | 0.719610 | 2.306296 | 0.934728 | -0.984321 | 0.504867 | -0.441008 | -2.767177 | 1.734671 | -1.988050 | 4.212046 | -2.797781 | -2.083279 | 0.342234 | -1.369170 | 2.094685 | 0.306836 | 5.488074 | -0.387737 | 0.088653 | 0.325817 | 0.122257 | 6.040354 | -1.381402 | 0.374942 | -2.734241 | 2.510217 | -1.072226 | -0.053562 | -1.292989 | 1.527720 | -0.496910 | 3.789736 | 1.130689 | 0.618278 | -0.111146 | 5.708912 | 1.542366 | -2.481019 | 0 |
| 18503 | -1.885467 | NaN | 3.401561 | 1.789738 | 0.133296 | -0.147678 | -1.045946 | -3.993286 | 0.635953 | 1.338226 | -2.375811 | -0.316753 | 0.141427 | -1.944057 | -2.353721 | -5.816310 | -0.266041 | 0.900483 | 3.391439 | -0.041519 | -3.138501 | 0.089603 | -3.924260 | 1.734113 | 0.065329 | 2.501768 | -0.067829 | 0.613998 | -1.745991 | -1.770933 | -1.143367 | -1.951922 | 0.057635 | 2.014103 | 1.450804 | 1.142613 | -0.738409 | 3.912079 | 2.290597 | -3.021343 | 0 |
| 18653 | 7.865920 | NaN | 9.759605 | -4.866189 | -2.831151 | -0.705419 | 0.070462 | -4.640092 | 1.605480 | 2.395803 | -1.351212 | 0.343210 | 5.042336 | -1.267126 | -1.022127 | -7.786885 | -7.093112 | -0.571394 | 1.909482 | -4.008349 | -9.502813 | 1.245014 | -7.552280 | -4.957937 | 4.076862 | 5.616998 | 10.132394 | -4.471888 | -3.724277 | -5.700213 | 5.476143 | -8.090465 | -2.305509 | -0.561274 | 4.582797 | 5.907647 | -2.311508 | -0.758131 | 2.801881 | -0.528910 | 0 |
| 18853 | 2.662045 | NaN | 2.980068 | 4.430762 | -0.237769 | 0.671919 | 0.380068 | -7.646684 | 4.434754 | -0.746393 | -1.169098 | -3.067151 | 0.025311 | -3.767079 | -1.930625 | -10.297648 | 0.341456 | -1.307246 | 4.457440 | -2.175016 | -5.359654 | 1.256763 | -5.030203 | 0.453526 | 0.703293 | 6.002827 | 0.908615 | 1.179818 | -2.527061 | -4.018170 | -4.606549 | -5.493590 | -1.104656 | 1.224987 | 0.975596 | -4.794411 | -2.269039 | 7.670648 | 0.824983 | -3.929104 | 0 |
| 18974 | -2.354134 | NaN | 2.053893 | 0.811660 | 2.540366 | -0.924875 | -0.208380 | -0.562864 | -0.140210 | -2.146916 | -3.838010 | 2.682121 | -0.660400 | -2.518779 | -1.708471 | -2.675250 | 3.630102 | 2.292712 | -0.160406 | -0.367917 | -1.413597 | 0.225356 | 0.242855 | 2.928137 | -0.189872 | 4.111134 | -4.003064 | -0.160378 | -0.929010 | -1.678050 | -0.041593 | -0.621103 | -0.896509 | -1.181480 | -1.236617 | 1.237120 | 1.228277 | 2.073727 | 1.223874 | 1.472175 | 0 |
| 19814 | -1.763501 | NaN | 2.845012 | -2.753083 | -0.811848 | -0.101166 | -1.382141 | -1.105042 | -0.054339 | 0.159742 | 0.639614 | 2.035117 | 4.863495 | -0.351239 | -4.248906 | -1.556507 | -3.843409 | 1.644293 | -0.470551 | -0.325697 | -3.333757 | -0.352452 | -1.690070 | -3.143352 | -0.703340 | 1.791279 | 1.292887 | -2.778917 | 0.839832 | 1.251279 | 0.264272 | -2.158880 | 1.859682 | -0.337278 | 1.509300 | 3.408411 | 0.922594 | -1.502959 | 2.514666 | -0.793574 | 0 |
| 21355 | NaN | 1.376831 | -4.033973 | 6.223723 | -1.976194 | -3.358548 | 1.176538 | 2.062040 | 4.570544 | -4.255460 | -2.759210 | 2.169949 | 1.731442 | 0.540123 | -1.059905 | -1.880122 | 8.106797 | -2.347863 | 2.540221 | 8.765846 | -0.355708 | 2.241704 | 4.984922 | -3.225233 | 1.408811 | -3.249485 | -2.467805 | 3.640014 | -3.176068 | -1.933804 | -10.867789 | -4.770676 | -9.911617 | 4.513182 | -3.955824 | -6.031229 | 2.984531 | -2.164553 | -1.068433 | -2.513672 | 1 |
| 21515 | NaN | 0.450742 | 7.285960 | -1.348030 | -2.188183 | 0.337266 | -1.575823 | -5.081472 | 1.018192 | 1.610708 | -0.359950 | -0.595780 | 2.362292 | -2.549198 | -2.667852 | -7.695295 | -4.138587 | 0.719394 | 3.452604 | -1.501593 | -7.229416 | 0.114637 | -7.575537 | -1.947683 | 1.375210 | 3.513915 | 3.711396 | -2.058470 | 0.125092 | -0.627102 | 1.116175 | -5.706846 | 1.913019 | 0.516271 | 5.209631 | 2.453169 | -1.510589 | 1.823366 | 2.359554 | -2.064000 | 0 |
| 22308 | NaN | 1.032470 | 10.560475 | 2.922123 | -3.771827 | -3.597172 | -2.059798 | -4.421488 | 5.335619 | 0.666123 | -4.077535 | -2.392490 | 7.607601 | -0.806719 | -5.063959 | -9.516622 | -3.132732 | -3.190273 | 4.580984 | 3.970251 | -10.371776 | -0.542143 | -9.189041 | -7.022110 | 3.624067 | 5.614774 | 5.311457 | -2.868689 | -4.572863 | -5.229601 | 0.305796 | -7.648200 | -4.456419 | 2.827506 | 4.823345 | 2.531195 | -1.954694 | -4.547419 | 2.851387 | -5.803026 | 0 |
| 22382 | NaN | -0.666978 | 3.715829 | 4.934000 | 1.667596 | -4.356097 | -2.823137 | 0.373175 | -0.709951 | 2.177428 | -8.808378 | 2.562259 | 1.958947 | 0.004700 | -5.939770 | -4.675866 | 3.292160 | 1.975257 | 4.434138 | 4.712570 | -4.123662 | 1.047771 | 0.858821 | 6.752781 | -0.811892 | 1.876078 | -4.789271 | 1.248321 | -6.278302 | -2.252599 | 0.464499 | 6.663446 | -2.897697 | 3.068461 | 2.486862 | 4.808548 | 0.069305 | -1.215784 | 3.013674 | -5.972586 | 0 |
| 22489 | -4.860821 | NaN | 5.897369 | -2.177277 | -0.677935 | -1.257887 | -3.172854 | -0.245307 | -2.696845 | 4.022413 | -2.470921 | 1.480313 | 3.207869 | 0.699197 | -4.026274 | -1.125345 | -4.280574 | 2.265843 | 1.703712 | 1.579378 | -3.067421 | -1.534962 | -4.336092 | 0.390016 | -0.341929 | -0.060204 | 1.117587 | -2.141079 | -0.601314 | 1.099609 | 4.661311 | 1.849434 | 2.633613 | 1.747949 | 4.198521 | 8.332839 | -0.087613 | -3.040214 | 3.819440 | -2.715520 | 0 |
| 22650 | NaN | -2.561519 | -0.180836 | -7.194814 | -1.043832 | 1.384845 | 1.306093 | 1.559192 | -2.992173 | 1.274543 | 3.032965 | 3.689155 | 0.522447 | 0.753121 | 2.457162 | 3.191608 | -4.053648 | 1.523019 | -2.111864 | -3.494318 | 0.553895 | 0.755463 | 1.149794 | -2.128372 | 0.730518 | -2.165217 | 5.066172 | -2.036167 | 1.563127 | 0.856002 | 3.188166 | -2.531655 | 0.560392 | -1.153884 | -0.019205 | 4.065248 | 0.978880 | -0.571288 | 0.630374 | 3.919467 | 0 |
| 22993 | NaN | 1.299595 | 4.382858 | 1.583219 | -0.076564 | 0.658770 | -1.638530 | -4.814763 | -0.914819 | 2.811808 | 0.571760 | -0.318586 | 0.853250 | -2.777201 | -3.633432 | -5.401728 | -4.238895 | 0.261186 | 5.218141 | -3.446377 | -4.544353 | -0.523904 | -5.111638 | 3.632682 | -2.314757 | 4.269501 | -0.810190 | -0.532287 | 0.692526 | 1.787281 | 0.724201 | 1.772287 | 5.755242 | 1.203739 | 5.663939 | 0.413630 | -2.643934 | 5.529745 | 2.104536 | -4.945350 | 0 |
| 23385 | -2.270541 | NaN | 1.710061 | 1.157522 | -0.355177 | -5.449480 | -0.786321 | 3.936176 | -1.576138 | 0.800881 | -8.511607 | 8.425671 | 2.662447 | 0.696275 | -3.691554 | -3.227182 | 5.014306 | 2.677453 | 4.117016 | 5.919193 | -5.061017 | 4.174690 | 5.949463 | 4.687390 | 1.122701 | -1.937088 | -1.736193 | 1.306631 | -7.059369 | -2.439151 | -1.545940 | 2.651480 | -8.429332 | 3.511387 | 1.500102 | 5.552380 | 2.588580 | -3.453418 | 2.324339 | -2.760081 | 0 |
| 24403 | -3.274827 | NaN | 0.831948 | 5.575159 | 2.612391 | -4.015005 | -1.621194 | 3.759563 | 0.750664 | -1.356604 | -7.442354 | -0.668456 | -0.382625 | 1.153390 | -1.997498 | 0.591903 | 6.607529 | -0.031259 | -0.503674 | 5.332659 | 0.123626 | -0.223807 | 2.933815 | 4.541367 | 0.220799 | 1.355465 | -6.618571 | 1.035502 | -4.031089 | -2.070247 | 1.907879 | 7.452206 | -3.365121 | -0.604766 | -0.500495 | 0.951120 | 0.268142 | -5.436670 | -0.864503 | -1.246593 | 0 |
| 24531 | NaN | -2.326319 | -0.051978 | 0.615063 | -0.895755 | -2.437003 | 0.349826 | 2.092611 | -2.933523 | 2.291272 | -3.838480 | 6.294332 | -1.583866 | 0.012411 | 0.546823 | -0.998281 | 3.333004 | 1.319231 | 5.202879 | 3.559792 | -0.646710 | 2.200191 | 2.725374 | 4.346137 | 0.560001 | -4.238204 | -0.248635 | 2.953068 | -3.262247 | -0.752281 | -2.262264 | 0.134995 | -5.183424 | 5.251667 | 0.716371 | 3.210930 | 1.641985 | 1.543559 | 1.805163 | -2.039510 | 0 |
| 24757 | -1.663687 | NaN | -0.712286 | -4.346935 | 1.391670 | -0.093951 | -2.163175 | -0.380573 | 0.031191 | -0.658845 | -5.653376 | 2.888427 | 2.208387 | 0.551841 | -5.221309 | -5.362598 | 2.142361 | 8.082694 | -4.126662 | 1.703736 | -3.907940 | 4.499606 | 4.886048 | 2.087149 | 0.978603 | -1.480454 | -0.362217 | -0.817730 | -3.844418 | -1.255608 | -1.121747 | 0.306588 | -2.690990 | -3.111879 | -1.596402 | 5.821108 | 3.462033 | -1.736752 | 2.291092 | 2.240769 | 0 |
| 24979 | NaN | 0.752613 | -0.271099 | 1.301204 | 2.038697 | -1.485203 | -0.411939 | 0.980629 | 0.810336 | -0.065120 | -3.844253 | -1.009095 | 1.098136 | 1.431149 | -1.496626 | 0.017872 | 1.403325 | 0.468766 | -2.054625 | 0.628134 | 0.045158 | 0.565553 | 2.473152 | 1.881349 | 0.199960 | 1.756903 | -1.189882 | -0.287770 | -3.974009 | -3.101097 | 2.091916 | 4.410397 | -2.208567 | -1.358706 | -1.725697 | 1.679060 | -0.208564 | -2.335547 | 0.112248 | -0.542931 | 0 |
| 25124 | NaN | -3.470223 | 2.271168 | -5.002601 | 0.257416 | 2.143727 | -1.530032 | -1.882230 | -3.304110 | 2.936393 | 0.635756 | 1.244968 | -0.589304 | -0.508300 | -0.867560 | -0.523398 | -3.762884 | 4.072808 | -0.671308 | -1.950134 | 0.280864 | -0.975485 | -3.414090 | 0.605396 | -0.465734 | -1.316453 | 1.841977 | -1.127626 | 2.015002 | 1.615433 | 2.782480 | -1.514765 | 4.055140 | 0.191024 | 0.891056 | 6.167782 | 0.812102 | 2.179586 | 3.078261 | 1.274048 | 0 |
| 25735 | -4.711837 | NaN | 6.119761 | 1.958562 | 2.203888 | -0.951680 | -2.536187 | -4.508204 | 1.395311 | 2.139783 | -6.830801 | -3.161616 | 1.074234 | -0.527112 | -3.681045 | -5.898352 | 0.119857 | 2.037730 | 0.264365 | 1.917029 | -1.777201 | -2.024444 | -6.653541 | 0.743441 | 1.384434 | 4.250186 | 0.085643 | -0.351501 | -4.755355 | -6.113058 | 2.606589 | -1.484423 | -1.585838 | 1.731706 | -1.635805 | 6.295918 | -0.432775 | 0.981792 | 4.424316 | -2.725923 | 0 |
| 26283 | NaN | 2.056243 | 3.330642 | 2.741497 | 2.783166 | -0.444191 | -2.015376 | -0.887154 | -1.110920 | 0.025289 | -2.752568 | -1.147514 | -1.542587 | -2.020288 | -2.343747 | -1.388310 | 1.272082 | 1.224078 | 0.749635 | -0.924947 | -0.823329 | -1.865499 | -2.625731 | 5.157844 | -1.809266 | 4.433066 | -5.879356 | -0.431343 | 0.966286 | 1.189187 | 3.295126 | 5.112126 | 4.675408 | -1.709632 | 2.429762 | 0.996644 | -1.190509 | 1.207054 | 0.511023 | -0.884200 | 0 |
| 26295 | NaN | -0.292621 | 0.688971 | -1.345315 | 2.201480 | -1.637512 | -2.663417 | 1.607634 | -0.648152 | -1.488105 | -4.125804 | 3.362373 | 4.193800 | -0.252368 | -7.348832 | -2.806359 | 0.602045 | 5.259645 | -2.536021 | -0.365145 | -5.529912 | 3.075829 | 5.613231 | 4.546954 | -2.097352 | 3.094847 | -5.021768 | -2.597850 | -1.708906 | 2.430214 | 1.635446 | 7.600895 | 2.342042 | -5.093293 | 2.955816 | 3.166428 | 1.278396 | -3.542493 | 0.646174 | -0.038495 | 0 |
| 26930 | NaN | 2.704511 | 4.587169 | 1.867930 | 2.050133 | -0.925076 | -1.669496 | -1.653803 | -0.243383 | -0.317316 | -2.224121 | 0.258499 | 1.562079 | -2.227965 | -3.845563 | -2.398034 | -0.655984 | 0.636778 | 1.076331 | -1.442859 | -2.757933 | -1.739484 | -3.149502 | 2.459183 | -1.692056 | 6.165469 | -3.976617 | -1.734007 | 0.288984 | 0.199412 | 2.580065 | 2.527207 | 3.625279 | -1.200200 | 2.328028 | 1.666937 | -0.943228 | 0.946846 | 1.655145 | -1.665439 | 0 |
| 27340 | NaN | 4.296356 | 10.532402 | 4.884495 | -2.546487 | -3.385216 | -2.159064 | -6.145582 | 3.357127 | 1.921921 | -5.800924 | -0.692044 | 3.522976 | -3.883207 | -5.645438 | -15.328618 | -1.559307 | -1.154972 | 9.396666 | -0.857673 | -15.369426 | 3.793166 | -6.297913 | 4.101312 | 1.853589 | 7.896776 | 1.670772 | -1.099949 | -5.805309 | -3.066473 | 0.882695 | -0.274613 | -1.075785 | 0.607624 | 11.255453 | -0.464038 | -4.602301 | 1.925733 | 0.967101 | -7.689567 | 0 |
| 27595 | 0.861760 | NaN | 2.924622 | -1.963893 | 0.805864 | -3.192955 | -0.220083 | 4.040213 | -0.774395 | -3.076172 | -5.471740 | 6.092631 | 1.285354 | -0.906181 | -1.214490 | -0.846902 | 4.874224 | 2.900057 | -1.519839 | 2.555566 | -3.594689 | 1.965865 | 3.399015 | 0.933687 | 1.916750 | 1.405724 | -2.426604 | -1.578740 | -1.771624 | -1.260814 | 1.761120 | -1.081264 | -4.595465 | -2.271578 | 0.141192 | 4.006448 | 2.850131 | -4.916653 | 0.328659 | 4.028383 | 0 |
| 27612 | NaN | 3.144986 | 2.971159 | 4.075005 | -0.995566 | 0.470693 | -2.132163 | -5.388640 | 2.931227 | -0.335955 | -0.319313 | -3.364095 | 0.769284 | -3.261426 | -4.731906 | -9.831482 | -0.772805 | 0.448437 | 4.355107 | -1.767723 | -8.302013 | 2.418540 | -2.957517 | 3.566814 | -1.130283 | 4.407458 | -2.399675 | -0.020642 | 0.226860 | 2.440845 | -2.559616 | 1.820677 | 4.556897 | -2.038816 | 7.296905 | -5.146063 | -3.007760 | 3.589546 | -1.119321 | -4.575606 | 0 |
| 28449 | NaN | 7.038653 | 2.144536 | -3.201788 | 4.112972 | 3.375972 | -1.337179 | -4.546371 | 1.941427 | -5.466593 | 2.364140 | -1.338121 | 3.052379 | -4.598111 | -6.043440 | -4.133390 | -2.798889 | 4.435243 | -6.633056 | -8.542727 | -4.266666 | -0.382582 | -1.141347 | -0.153037 | -3.116467 | 11.243591 | -5.045617 | -5.440225 | 5.034689 | 2.807927 | 1.920413 | 0.157778 | 9.768106 | -10.258190 | 0.513864 | -1.974958 | -0.029436 | 3.127486 | 0.009482 | 4.538125 | 0 |
| 28657 | 2.014932 | NaN | -0.538848 | -4.622031 | 0.421291 | -0.786865 | 0.723106 | 3.490773 | -0.642281 | -4.620383 | -0.782914 | 6.614610 | 1.774801 | -1.305497 | -1.040675 | 0.514344 | 3.002089 | 3.636347 | -3.683410 | -0.210081 | -2.498245 | 2.625083 | 5.471032 | -1.186716 | 0.617094 | 0.661472 | -1.781620 | -2.222955 | 1.525736 | 1.591908 | -0.794278 | -2.872988 | -1.958172 | -4.175893 | -0.960785 | 1.190289 | 3.660581 | -3.013880 | -0.549200 | 6.020092 | 0 |
| 29660 | -0.111116 | NaN | 3.096279 | 0.198342 | -0.339936 | -1.814823 | -1.378926 | -0.815859 | 1.352388 | 1.791721 | -2.665404 | -1.052728 | 4.974548 | 1.672878 | -3.801691 | -2.170785 | -3.183549 | -0.369295 | -0.002157 | 0.869145 | -3.512223 | 0.154332 | -1.139785 | -1.566507 | 0.500155 | 2.251503 | 2.500450 | -1.964050 | -4.101954 | -2.796832 | 2.414100 | 1.831702 | -1.444323 | 0.326593 | 1.260694 | 3.732963 | -0.981232 | -3.546839 | 1.886337 | -3.362936 | 0 |
| 29928 | NaN | 4.439987 | 2.911522 | 4.902200 | -1.551156 | -2.086548 | 1.983949 | 1.499095 | 0.029891 | -2.401624 | 4.139131 | 3.193364 | 0.792014 | -2.972491 | 1.814184 | 3.643745 | 0.323347 | -7.138074 | 6.006540 | 0.272361 | 0.477709 | -4.085337 | -3.784087 | -2.863879 | -1.733863 | 4.106070 | -3.132371 | -0.109309 | 4.725421 | 2.566095 | -2.102183 | -3.445539 | 1.161856 | 3.860585 | 2.284675 | -5.555024 | -1.057429 | 1.291154 | -0.830806 | -2.214270 | 1 |
| 31171 | NaN | 1.382556 | 3.236896 | -3.818363 | -1.917264 | 0.437686 | 1.347540 | -2.036067 | 1.155712 | 0.306502 | 2.234228 | 0.628065 | 3.355933 | -0.483049 | 0.547765 | -2.161980 | -5.072360 | -1.412643 | -0.092420 | -3.925248 | -4.031637 | 0.784090 | -2.562611 | -4.673719 | 1.766502 | 2.998280 | 6.633352 | -2.927075 | -0.686811 | -2.376300 | 2.066104 | -5.414599 | -0.896510 | -1.057864 | 1.417365 | 1.161990 | -1.147123 | -0.048258 | 0.604532 | 0.814557 | 0 |
| 31946 | 0.768122 | NaN | 5.296110 | 0.043018 | -1.173729 | -2.248575 | 0.956395 | -0.089941 | -0.241678 | -1.061413 | -2.449467 | 5.085687 | 0.433722 | -2.632538 | 0.849103 | -2.630984 | 2.178473 | -0.844691 | 3.863689 | 1.723212 | -2.993629 | -0.465887 | -3.443891 | -1.774554 | 2.113390 | 2.186688 | 0.925808 | -0.191505 | -0.632520 | -2.589251 | -0.803008 | -7.720265 | -4.518617 | 3.182253 | 0.453452 | 2.175494 | 1.261707 | 0.892630 | 2.026732 | 0.632903 | 0 |
| 32492 | NaN | 1.609500 | 13.117066 | -4.925400 | -4.141238 | -0.619527 | -0.539598 | -6.960998 | 2.566956 | 2.579342 | -1.646912 | -0.222731 | 5.550131 | -2.624616 | -1.636796 | -11.493018 | -7.866266 | -0.548843 | 3.655900 | -3.627526 | -12.471740 | 1.071673 | -11.577760 | -6.600366 | 5.241508 | 6.715766 | 11.688238 | -4.950618 | -3.364108 | -6.198116 | 5.062374 | -12.272497 | -2.288350 | 0.163367 | 6.455313 | 6.356980 | -2.684643 | -0.039118 | 3.674137 | -1.123228 | 0 |
| 33169 | 8.477253 | NaN | 12.813848 | -0.114379 | -5.670578 | -0.753263 | -0.878347 | -10.028754 | 4.914955 | 1.355373 | -2.066374 | -0.556885 | 4.206234 | -5.118155 | -3.006336 | -17.446693 | -4.296532 | -1.023496 | 8.676091 | -0.708527 | -15.070218 | 2.358303 | -13.025111 | -5.577046 | 5.092785 | 6.161384 | 9.265314 | -1.956148 | -3.029534 | -5.159141 | -2.379599 | -15.933229 | -3.515683 | 3.213755 | 7.897210 | 1.030108 | -2.572983 | 4.312847 | 3.439016 | -4.465373 | 0 |
| 33242 | NaN | 3.933815 | -0.761930 | 2.651889 | 1.753614 | -0.554092 | 1.829107 | -0.105409 | -3.737081 | 1.036776 | -0.358778 | 5.858726 | -4.206063 | -3.348732 | 1.476003 | -0.451199 | 2.342220 | -0.376373 | 6.431173 | -3.528702 | 0.458085 | 0.970149 | 2.184753 | 8.724416 | -2.764178 | 1.918974 | -4.302981 | 2.849464 | -0.028793 | 1.115860 | -1.477069 | 3.486408 | 1.028094 | 2.845747 | 1.744060 | -1.999615 | -0.783041 | 8.698449 | 0.352489 | -2.005397 | 0 |
| 33918 | NaN | -0.561572 | 1.304694 | 3.448639 | 1.852764 | -3.194378 | -2.175512 | 1.098184 | -1.699582 | 1.646866 | -6.972954 | 3.400340 | 0.428787 | -0.278516 | -4.797700 | -3.647620 | 3.428038 | 2.915472 | 3.570395 | 2.687164 | -3.514757 | 2.323449 | 3.470902 | 8.368273 | -1.498822 | 0.763059 | -5.320108 | 1.471741 | -4.719419 | -0.184526 | 0.176885 | 8.050687 | -1.288005 | 1.284990 | 2.897762 | 3.187620 | 0.205473 | 0.003587 | 1.609036 | -4.205113 | 0 |
| 33951 | NaN | -1.443383 | 2.546169 | 0.645181 | 1.307618 | -2.431704 | -3.674644 | 0.342241 | 0.389836 | -0.306251 | -5.905261 | 1.750608 | 4.654257 | 0.100310 | -8.229944 | -4.931795 | 0.936420 | 4.668718 | -0.676050 | 2.598823 | -6.298749 | 2.358325 | 2.712579 | 3.585090 | -1.191082 | 2.361384 | -4.307813 | -1.789776 | -3.158587 | 1.057995 | 0.714463 | 6.059416 | 0.775165 | -2.541952 | 3.307488 | 4.113729 | 1.028314 | -4.126723 | 1.785498 | -2.489193 | 0 |
| 35506 | NaN | 1.420973 | 0.777263 | -0.374181 | 0.220099 | -1.565053 | -0.388464 | 0.401173 | -0.615000 | -1.313337 | -0.808852 | 5.855235 | 4.077151 | -1.679104 | -4.961079 | -2.427831 | -0.412295 | 1.768088 | 1.878866 | -0.945177 | -4.754032 | 2.144357 | 3.460622 | 1.841588 | -2.146834 | 2.994645 | -2.609448 | -1.371366 | -0.495147 | 2.002637 | -2.130182 | 1.553486 | 0.531795 | -0.539355 | 2.391643 | 0.492160 | 1.137505 | 0.716497 | 1.348849 | -1.685738 | 0 |
| 35864 | NaN | 1.158675 | 1.185685 | -0.262184 | 3.471793 | 0.076985 | -4.273866 | -2.705619 | 1.586950 | -1.852604 | -5.098585 | -0.865445 | 4.231747 | -1.280276 | -10.284737 | -7.690933 | 0.299679 | 7.390036 | -3.971822 | -1.322928 | -7.036112 | 3.219237 | 3.268226 | 5.201635 | -2.502753 | 5.556055 | -6.165258 | -2.712468 | -2.041795 | 1.745245 | 0.510390 | 7.473777 | 4.862734 | -7.056084 | 2.588199 | 2.236311 | 0.647856 | -1.052416 | 1.282013 | -0.947339 | 0 |
| 35996 | -1.482740 | NaN | -1.295792 | 6.356887 | -1.753139 | -3.009627 | -1.837381 | 1.034511 | 1.091602 | 0.222193 | -1.337225 | 1.456780 | 2.988041 | -0.074370 | -5.723930 | -3.860605 | 1.489512 | -1.121984 | 6.185880 | 3.379680 | -5.976499 | 3.315692 | 4.590666 | 5.041620 | -2.825506 | -0.542547 | -5.022057 | 1.640321 | -1.752756 | 4.594651 | -4.905535 | 8.203325 | 0.801515 | 1.514732 | 6.920738 | -4.909778 | -1.343086 | -1.272598 | -1.573322 | -7.573068 | 0 |
| 36238 | NaN | 2.582267 | 5.677477 | 1.551372 | -0.077294 | -2.880871 | -1.018954 | 0.425800 | 1.200203 | -0.290579 | -4.378552 | -0.487327 | 1.668028 | -0.934890 | -1.812017 | -4.789462 | 0.354191 | -0.681784 | 1.568698 | -1.097806 | -7.768400 | 2.153539 | -0.584593 | 3.194908 | 1.554545 | 5.206579 | -0.443332 | -2.208535 | -3.314805 | -1.699153 | 5.261297 | 4.211412 | -0.545175 | -3.610752 | 6.305429 | 0.666642 | -2.682559 | -3.893799 | -1.569401 | -0.942481 | 0 |
| 36283 | NaN | 1.492173 | 2.659206 | 0.222784 | -0.303648 | -1.347322 | 0.044309 | -0.159095 | 1.108116 | -0.572670 | -2.280958 | 0.315663 | 1.005142 | -0.494825 | -0.359722 | -2.628642 | 0.660794 | -0.311087 | 0.489696 | 0.091892 | -3.321747 | 1.033245 | -0.597830 | -0.153678 | 1.546904 | 2.155339 | 0.984093 | -0.862502 | -2.067232 | -2.184193 | 1.338870 | -1.007343 | -2.229579 | -0.870845 | 1.299595 | 0.667952 | -0.503349 | -1.485419 | -0.153722 | 0.156501 | 0 |
| 36731 | -0.928572 | NaN | 2.375506 | -1.236914 | 3.228744 | -2.100088 | -2.189908 | 0.588644 | 1.955973 | -5.008491 | -7.387682 | 3.314466 | 3.774141 | -1.836451 | -7.098950 | -6.071154 | 4.891622 | 6.479283 | -4.841316 | 0.968315 | -6.694007 | 3.470100 | 4.668375 | 2.431557 | 0.398758 | 5.752028 | -5.572299 | -2.881601 | -2.985890 | -1.455391 | 0.332569 | 1.613181 | -1.820569 | -6.664808 | -0.455080 | 3.054891 | 2.935276 | -3.791135 | 0.863011 | 3.335753 | 0 |
| 36733 | -2.377369 | NaN | -0.009173 | -1.471979 | 1.295482 | 0.724894 | -1.122797 | -3.190475 | 3.250575 | -4.861648 | -0.685484 | 2.360250 | 5.431774 | -2.508028 | -7.249666 | -5.570837 | 0.679271 | 4.390615 | -3.424351 | -0.273154 | -4.232664 | 1.505401 | 1.569639 | -3.371761 | -1.287601 | 4.812515 | -2.778094 | -2.349831 | 0.684132 | 0.351316 | -5.729277 | -5.093149 | 0.439355 | -3.167241 | -2.713266 | -0.592845 | 3.229219 | 1.315635 | 2.282838 | 1.151589 | 0 |
| 37469 | 3.318285 | NaN | 4.764121 | 0.239790 | -2.088341 | -1.334183 | -1.326659 | -1.931971 | 0.876763 | 0.348388 | -3.028446 | 1.244556 | 0.670343 | -2.023985 | -2.076811 | -7.758867 | 0.721137 | 1.523690 | 3.864712 | 0.973309 | -7.914032 | 2.972878 | -2.157735 | 1.690387 | 1.885813 | 1.036972 | 1.036667 | -0.322014 | -1.652494 | -0.069723 | -0.353948 | -2.227943 | -1.213178 | -0.208744 | 5.786150 | 0.718919 | -0.701807 | -0.134913 | 0.211544 | -1.405950 | 0 |
| 37815 | -0.119181 | NaN | 3.657612 | -1.231802 | 1.946873 | -0.119089 | 0.652414 | -1.490208 | -0.033631 | -2.556604 | -2.093721 | 2.938705 | -0.488531 | -3.371921 | -0.236224 | -2.676248 | 1.934337 | 1.646518 | -0.602926 | -2.326010 | -1.779261 | -0.465891 | -2.085992 | 0.332918 | 0.671434 | 5.423404 | -1.576116 | -1.345127 | 0.404331 | -2.333296 | 0.960273 | -4.670353 | -0.593916 | -1.650592 | -1.405071 | 1.531267 | 1.079147 | 2.832949 | 1.450781 | 3.232659 | 0 |
| 38038 | NaN | 5.469351 | 3.925862 | 7.075116 | -0.581082 | -5.171569 | 1.415715 | 1.564463 | -1.076731 | 0.971791 | -2.842272 | 5.672038 | 0.840100 | -2.738785 | -0.999742 | -3.128321 | 1.468569 | -5.253634 | 10.643027 | -1.660331 | -6.989902 | 2.244110 | 2.017966 | 8.153053 | -1.742839 | 5.856712 | -3.550853 | 1.102751 | -3.914045 | -0.257994 | 0.644851 | 7.125148 | -1.795369 | 2.989587 | 8.169314 | -3.717625 | -3.768507 | 2.539606 | -1.295166 | -6.938777 | 1 |
| 38479 | -3.323643 | NaN | -0.550547 | -0.289982 | -0.723397 | 1.715816 | 1.084434 | -1.546470 | -1.995773 | 0.764524 | 5.863686 | 2.532624 | 0.156751 | -1.895069 | 0.392496 | 2.513755 | -3.744185 | -1.906895 | 3.287043 | -2.974138 | 2.220764 | -2.479591 | -2.450791 | -1.212548 | -3.023165 | 0.675593 | -0.249118 | 0.146150 | 4.726370 | 3.647123 | -2.515131 | -2.481974 | 4.541512 | 3.076467 | 0.522451 | -1.977151 | -0.205159 | 5.612268 | 1.354003 | -1.709427 | 0 |
missing_data.shape
(85, 41)
From the count of the missing data, V1 and V2, the missing data pattern on V1=46 and V2=39 is independent of each other.
final_data_for_model = data.copy()
X = final_data_for_model.drop("Target", axis=1) # Features
y = final_data_for_model["Target"] #Label (Target Variable)
# Splitting data into training and validation set:
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=1)
print(X_train.shape, X_val.shape)
print(y_train.shape, y_val.shape)
(28000, 40) (12000, 40) (28000,) (12000,)
print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in validation data =", X_val.shape[0])
Number of rows in train data = 28000 Number of rows in validation data = 12000
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_val.value_counts(normalize=True))
Percentage of classes in training set: 0 0.944893 1 0.055107 Name: Target, dtype: float64 Percentage of classes in test set: 0 0.946333 1 0.053667 Name: Target, dtype: float64
X_test = data_test.drop("Target", axis=1) # Features
y_test = data_test["Target"] #Label (Target Variable)
imputer = SimpleImputer(strategy="median")
impute = imputer.fit(X_train)
X_train = impute.transform(X_train)
X_val = imputer.transform(X_val)
X_test = imputer.transform(X_test)
Minimum cost/Cost associated with modelLet's create two functions to calculate different metrics and confusion matrix, so that we don't have to use the same code repeatedly for each model.
# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
"""
TP = confusion_matrix(target, model.predict(predictors))[1, 1]
FP = confusion_matrix(target, model.predict(predictors))[0, 1]
FN = confusion_matrix(target, model.predict(predictors))[1, 0]
Cost = TP * 15 + FP * 5 + FN * 40 # maintenance cost by using model
Min_Cost = (TP + FN) * 15 # minimum possible maintenance cost = number of actual positives
Percent = Min_Cost / Cost # ratio of minimum possible maintenance cost and maintenance cost by model
# predicting using the independent variables
pred = model.predict(predictors)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{
"Accuracy": acc,
"Recall": recall,
"Precision": precision,
"F1": f1,
"Minimum_Vs_Model_cost": Percent,
},
index=[0],
)
return df_perf
def confusion_matrix_sklearn(model, predictors, target):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
"""
y_pred = model.predict(predictors)
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
TP*(Repair cost) + FN*(Replacement cost) + FP*(Inspection cost)Eventually, all 3 metrics will do the same work in the backend and the only difference will be in the scale of the values of the metric.
The metric provided in the next cell is to maximize(minimum possible maintenance cost/maintenance cost)
# defining metric to be used for optimization and with cross-validation
def Minimum_Vs_Model_cost(y_train, y_pred):
"""
We want the model to optimize the maintenance cost and reduce it to the lowest possible value.
The lowest possible maintenance cost will be achieved when each sample is predicted correctly.
In such a scenario, the maintenance cost will be the total number of failures times the maintenance cost of replacing one generator,
which is given by (TP + FN) * 40 (i.e., the actual positives*40).
For any other scenario,
the maintenance cost associated with the model will be given by (TP * 15 + FP * 5 + FN * 40).
We will use the ratio of these two maintenance costs as the cost function for our model.
The greater the ratio, the lower the associated maintenance cost and the better the model.
"""
TP = confusion_matrix(y_train, y_pred)[1, 1]
FP = confusion_matrix(y_train, y_pred)[0, 1]
FN = confusion_matrix(y_train, y_pred)[1, 0]
return ((TP + FN) * 15) / (TP * 15 + FP * 5 + FN * 40)
# A value of .80 here, will represent that the minimum maintenance cost is 80% of the maintenance cost associated with the model.
# Since minimum maintenance cost is constant for any data, when minimum cost will become 100% of maintenance cost associated with the model
# Model will have give the least possible maintenance cost.
# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Higher the values, the lower the maintenance cost
# There are different solvers available in Sklearn logistic regression
# The newton-cg solver is faster for high-dimensional data
model = LogisticRegression(solver="newton-cg", random_state=1)
lg = model.fit(X_train, y_train)
lg
LogisticRegression(random_state=1, solver='newton-cg')
#Calculating different metrics
lg_model_train_perf=model_performance_classification_sklearn(lg, X_train, y_train)
print("Training performance:\n",lg_model_train_perf)
lg_model_val_perf=model_performance_classification_sklearn(lg, X_val, y_val)
print("Validation performance:\n",lg_model_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(lg, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.966214 0.473752 0.845087 0.607143 0.524651
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9675 0.487578 0.839572 0.616896 0.530478
#Fitting the model
d_tree = DecisionTreeClassifier(random_state=1)
d_tree.fit(X_train,y_train)
#Calculating different metrics
dtree_model_train_perf=model_performance_classification_sklearn(d_tree,X_train,y_train)
print("Training performance:\n",dtree_model_train_perf)
dtree_model_val_perf=model_performance_classification_sklearn(d_tree,X_val,y_val)
print("Validation performance:\n",dtree_model_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(d_tree, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 1.0 1.0 1.0 1.0 1.0
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.971083 0.768634 0.714286 0.740464 0.672
#Fitting the model
rf_estimator = RandomForestClassifier(random_state=1)
rf_estimator.fit(X_train,y_train)
#Calculating different metrics
rf_estimator_model_train_perf=model_performance_classification_sklearn(rf_estimator,X_train,y_train)
print("Training performance:\n",rf_estimator_model_train_perf)
rf_estimator_model_val_perf=model_performance_classification_sklearn(rf_estimator,X_val,y_val)
print("Validation performance:\n",rf_estimator_model_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 1.0 1.0 1.0 1.0 1.0
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.987583 0.779503 0.986248 0.870772 0.729332
#base_estimator for bagging classifier is a decision tree by default
bagging_estimator=BaggingClassifier(random_state=1)
bagging_estimator.fit(X_train,y_train)
BaggingClassifier(random_state=1)
bagging_estimator_train_score=model_performance_classification_sklearn(bagging_estimator,X_train,y_train)
print("Training performance:\n",bagging_estimator_train_score)
bagging_estimator_val_score=model_performance_classification_sklearn(bagging_estimator,X_val,y_val)
print("Validation performance:\n",bagging_estimator_val_score)
#Creating confusion matrix
confusion_matrix_sklearn(bagging_estimator, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.997821 0.960467 1.0 0.979835 0.938184
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.98525 0.753106 0.964215 0.845684 0.703825
abc = AdaBoostClassifier(random_state=1)
abc.fit(X_train,y_train)
AdaBoostClassifier(random_state=1)
abc_train_score=model_performance_classification_sklearn(abc,X_train,y_train)
print("Training performance:\n",abc_train_score)
abc_val_score=model_performance_classification_sklearn(abc,X_val,y_val)
print("Validation performance:\n",abc_val_score)
#Creating confusion matrix
confusion_matrix_sklearn(abc, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.975643 0.624109 0.904225 0.738497 0.606605
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.975667 0.619565 0.894619 0.73211 0.602996
gbc = GradientBoostingClassifier(random_state=1)
gbc.fit(X_train,y_train)
GradientBoostingClassifier(random_state=1)
gbc_train_score=model_performance_classification_sklearn(gbc,X_train,y_train)
print("Training performance:\n",gbc_train_score)
gbc_val_score=model_performance_classification_sklearn(gbc,X_val,y_val)
print("Validation performance:\n",gbc_val_score)
#Creating confusion matrix
confusion_matrix_sklearn(gbc, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.987321 0.786131 0.979806 0.872348 0.734296
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.983917 0.731366 0.959267 0.829956 0.685836
xgb = XGBClassifier(random_state=1,eval_metric='logloss')
xgb.fit(X_train,y_train)
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
gamma=0, gpu_id=-1, importance_type='gain',
interaction_constraints='', learning_rate=0.300000012,
max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,
monotone_constraints='()', n_estimators=100, n_jobs=8,
num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=1,
scale_pos_weight=1, subsample=1, tree_method='exact',
validate_parameters=1, verbosity=None)
xgb_train_score=model_performance_classification_sklearn(xgb,X_train,y_train)
print("Training performance:\n",xgb_train_score)
xgb_val_score=model_performance_classification_sklearn(xgb,X_val,y_val)
print("Validation performance:\n",xgb_val_score)
#Creating confusion matrix
confusion_matrix_sklearn(xgb, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 1.0 1.0 1.0 1.0 1.0
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.99 0.829193 0.981618 0.89899 0.775281
# Store the score for all the models
All_Results = {}
X = X_train
y = y_train
data_sample = '_original_data'
models = [] # Empty list to store all the models
# Appending models into the list
models.append(("LogisticRegression", LogisticRegression(random_state=1, solver='newton-cg')))
models.append(("DecisionTree", DecisionTreeClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))
results = [] # Empty list to store all model's CV scores
names = [] # Empty list to store name of the models
# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")
for name, model in models:
scoring = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
kfold = StratifiedKFold(
n_splits=10, shuffle=True, random_state=1
) # Setting number of splits equal to 10
cv_result = cross_val_score(
estimator=model, X=X, y=y, scoring=scoring, cv=kfold
)
results.append(cv_result)
names.append(name)
score = cv_result.mean() * 100
print("{}: {}".format(name, score))
dict_key = name + data_sample
All_Results[dict_key] = score
print("\n" "Performance in Validation data:" "\n")
for name, model in models:
model.fit(X, y)
score = model_performance_classification_sklearn(model,X_val,y_val)
scores = score.iloc[0]['Minimum_Vs_Model_cost'] * 100
print("{}: {}".format(name, scores))
dict_key = name + data_sample
dict_key = dict_key + '_Validation'
All_Results[dict_key] = scores
Cross-Validation Performance: LogisticRegression: 52.35892533471536 DecisionTree: 65.10054473324213 Random forest: 71.53861239850332 Bagging: 69.43885217772146 Adaboost: 58.79589095944772 GBM: 68.1172554037155 Xgboost: 77.45266458451022 Performance in Validation data: LogisticRegression: 53.047775947281714 DecisionTree: 67.2 Random forest: 72.93318233295584 Bagging: 70.38251366120218 Adaboost: 60.2996254681648 GBM: 68.58359957401491 Xgboost: 77.52808988764045
# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))
fig.suptitle("Algorithm Comparison")
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
Percentage of classes in training set: 0 0.944893 1 0.055107 Name: Target, dtype: float64
# Synthetic Minority Over Sampling Technique
sm = SMOTE(sampling_strategy=1, k_neighbors=5, random_state=1)
X_train_over, y_train_over = sm.fit_resample(X_train, y_train)
print("Percentage of classes in training set:")
print(y_train_over.value_counts(normalize=True))
Percentage of classes in training set: 0 0.5 1 0.5 Name: Target, dtype: float64
X_train_over.shape
(52914, 40)
X = X_train_over
y = y_train_over
data_sample = '_oversampled'
models = [] # Empty list to store all the models
# Appending models into the list
models.append(("LogisticRegression", LogisticRegression(random_state=1, solver='newton-cg')))
models.append(("DecisionTree", DecisionTreeClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))
results = [] # Empty list to store all model's CV scores
names = [] # Empty list to store name of the models
# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")
for name, model in models:
scoring = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
kfold = StratifiedKFold(
n_splits=10, shuffle=True, random_state=1
) # Setting number of splits equal to 10
cv_result = cross_val_score(
estimator=model, X=X, y=y, scoring=scoring, cv=kfold
)
results.append(cv_result)
names.append(name)
score = cv_result.mean() * 100
print("{}: {}".format(name, score))
dict_key = name + data_sample
All_Results[dict_key] = score
print("\n" "Performance in Validation data:" "\n")
for name, model in models:
model.fit(X, y)
score = model_performance_classification_sklearn(model,X_val,y_val)
scores = score.iloc[0]['Minimum_Vs_Model_cost'] * 100
print("{}: {}".format(name, scores))
dict_key = name + data_sample
dict_key = dict_key + '_Validation'
All_Results[dict_key] = scores
Cross-Validation Performance: LogisticRegression: 79.40984786587808 DecisionTree: 94.01861217925251 Random forest: 97.1952035999941 Bagging: 95.75355501921703 Adaboost: 83.19322841604645 GBM: 87.28991668797381 Xgboost: 97.58309648832963 Performance in Validation data: LogisticRegression: 50.536228093120585 DecisionTree: 63.240589198036 Random forest: 80.836820083682 Bagging: 74.88372093023256 Adaboost: 57.79240203410111 GBM: 73.20954907161804 Xgboost: 80.66805845511482
# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))
fig.suptitle("Algorithm Comparison")
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
We can see that XGBOOST is giving the highest cross-validated recall followed by RandomForest. RandomForest and XGBoost can be potential candidates for performance tuning.
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
Percentage of classes in training set: 0 0.944893 1 0.055107 Name: Target, dtype: float64
# Random undersampler for under sampling the data
rus = RandomUnderSampler(random_state=1, sampling_strategy=1)
X_train_un, y_train_un = rus.fit_resample(X_train, y_train)
print("Percentage of classes in training set:")
print(y_train_un.value_counts(normalize=True))
Percentage of classes in training set: 0 0.5 1 0.5 Name: Target, dtype: float64
X = X_train_un
y = y_train_un
data_sample = '_undersampled'
models = [] # Empty list to store all the models
# Appending models into the list
models.append(("LogisticRegression", LogisticRegression(random_state=1, solver='newton-cg')))
models.append(("DecisionTree", DecisionTreeClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))
results = [] # Empty list to store all model's CV scores
names = [] # Empty list to store name of the models
# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")
for name, model in models:
scoring = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
kfold = StratifiedKFold(
n_splits=10, shuffle=True, random_state=1
) # Setting number of splits equal to 10
cv_result = cross_val_score(
estimator=model, X=X, y=y, scoring=scoring, cv=kfold
)
results.append(cv_result)
names.append(name)
score = cv_result.mean() * 100
print("{}: {}".format(name, score))
dict_key = name + data_sample
All_Results[dict_key] = score
print("\n" "Performance in Validation data:" "\n")
for name, model in models:
model.fit(X, y)
score = model_performance_classification_sklearn(model,X_val,y_val)
scores = score.iloc[0]['Minimum_Vs_Model_cost'] * 100
print("{}: {}".format(name, scores))
dict_key = name + data_sample
dict_key = dict_key + 'Validation'
All_Results[dict_key] = scores
Cross-Validation Performance: LogisticRegression: 77.96837541564223 DecisionTree: 77.62112457810912 Random forest: 84.84438354374095 Bagging: 81.74829074330015 Adaboost: 80.08772346318565 GBM: 83.40938371506861 Xgboost: 84.87637818095986 Performance in Validation data: LogisticRegression: 48.80020207123011 DecisionTree: 45.967166309778726 Random forest: 71.92851824274014 Bagging: 67.01352757544224 Adaboost: 53.28185328185329 GBM: 65.33648968549205 Xgboost: 75.1458576429405
# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))
fig.suptitle("Algorithm Comparison")
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
We can see that XGBOOST is giving the highest cross-validated recall followed by RandomForest.
All_Results
{'LogisticRegression_original_data': 52.35892533471536,
'DecisionTree_original_data': 65.10054473324213,
'Random forest_original_data': 71.53861239850332,
'Bagging_original_data': 69.43885217772146,
'Adaboost_original_data': 58.79589095944772,
'GBM_original_data': 68.1172554037155,
'Xgboost_original_data': 77.45266458451022,
'LogisticRegression_original_data_Validation': 53.047775947281714,
'DecisionTree_original_data_Validation': 67.2,
'Random forest_original_data_Validation': 72.93318233295584,
'Bagging_original_data_Validation': 70.38251366120218,
'Adaboost_original_data_Validation': 60.2996254681648,
'GBM_original_data_Validation': 68.58359957401491,
'Xgboost_original_data_Validation': 77.52808988764045,
'LogisticRegression_oversampled': 79.40984786587808,
'DecisionTree_oversampled': 94.01861217925251,
'Random forest_oversampled': 97.1952035999941,
'Bagging_oversampled': 95.75355501921703,
'Adaboost_oversampled': 83.19322841604645,
'GBM_oversampled': 87.28991668797381,
'Xgboost_oversampled': 97.58309648832963,
'LogisticRegression_oversampled_Validation': 50.536228093120585,
'DecisionTree_oversampled_Validation': 63.240589198036,
'Random forest_oversampled_Validation': 80.836820083682,
'Bagging_oversampled_Validation': 74.88372093023256,
'Adaboost_oversampled_Validation': 57.79240203410111,
'GBM_oversampled_Validation': 73.20954907161804,
'Xgboost_oversampled_Validation': 80.66805845511482,
'LogisticRegression_undersampled': 77.96837541564223,
'DecisionTree_undersampled': 77.62112457810912,
'Random forest_undersampled': 84.84438354374095,
'Bagging_undersampled': 81.74829074330015,
'Adaboost_undersampled': 80.08772346318565,
'GBM_undersampled': 83.40938371506861,
'Xgboost_undersampled': 84.87637818095986,
'LogisticRegression_undersampledValidation': 48.80020207123011,
'DecisionTree_undersampledValidation': 45.967166309778726,
'Random forest_undersampledValidation': 71.92851824274014,
'Bagging_undersampledValidation': 67.01352757544224,
'Adaboost_undersampledValidation': 53.28185328185329,
'GBM_undersampledValidation': 65.33648968549205,
'Xgboost_undersampledValidation': 75.1458576429405}
3 'Xgboost_undersampled'
The reason is of the models from original Data, over Sampled Data and UnderSampled Data the above three have generalized behaviour ( The difference between training and validation is less )
param_grid={'n_estimators':np.arange(150,300,50),'scale_pos_weight':[5,10], 'learning_rate':[0.1,0.2], 'gamma':[0,3,5], 'subsample':[0.8,0.9]}
param_grid = { "init": [AdaBoostClassifier(random_state=1),DecisionTreeClassifier(random_state=1)], "n_estimators": np.arange(75,150,25), "learning_rate": [0.2, 0.05, 1], "subsample":[0.5,0.7], "max_features":[0.5,0.7]}
param_grid = { "n_estimators": np.arange(10, 110, 20), "learning_rate": [ 0.2, 0.05, 1], "base_estimator": [ DecisionTreeClassifier(max_depth=1, random_state=1), DecisionTreeClassifier(max_depth=2, random_state=1), DecisionTreeClassifier(max_depth=3, random_state=1)]}
param_grid = {'C': np.arange(0.1,1.1,0.1)}
param_grid = { 'max_samples': [0.8,0.9], 'max_features': [0.8,0.9], 'n_estimators' : [40,50]}
param_grid = { "n_estimators": [150,250], "min_samples_leaf": np.arange(1, 3), "max_features": ['sqrt','log2'], "max_samples": np.arange(0.2, 0.6, 0.1)}
param_grid = {'max_depth': np.arange(2,20), 'min_samples_leaf': [1, 2, 5, 7], 'max_leaf_nodes' : [5, 10,15], 'min_impurity_decrease': [0.0001,0.001] }
%%time
# Choose the type of classifier.
rf_estimator_tuned = RandomForestClassifier(random_state=1)
# Grid of parameters to choose from
rf_param_grid = { "n_estimators": [150,250],
"min_samples_leaf": np.arange(1, 3),
"max_features": ['sqrt','log2'],
"max_samples": np.arange(0.2, 0.6, 0.1)
}
# Type of scoring used to compare parameter combinations
acc_scorer = scoring = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = GridSearchCV(rf_estimator_tuned, rf_param_grid, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)
# Set the clf to the best combination of parameters
rf_estimator_tuned = grid_obj.best_estimator_
# Fit the best algorithm to the data.
rf_estimator_tuned.fit(X_train, y_train)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Best parameters are {'max_features': 'sqrt', 'max_samples': 0.5000000000000001, 'min_samples_leaf': 1, 'n_estimators': 150} with CV score=0.6920011507337778:
Wall time: 29min 22s
#Calculating different metrics
rf_tuned_train_perf=model_performance_classification_sklearn(rf_estimator_tuned,X_train,y_train)
print("Training performance:\n",rf_tuned_train_perf)
rf_tuned_val_perf=model_performance_classification_sklearn(rf_estimator_tuned,X_val,y_val)
print("Validation performance:\n",rf_tuned_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator_tuned, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.99325 0.879456 0.997794 0.934895 0.832255
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.986833 0.765528 0.986 0.861888 0.717149
rf_tuned_test_perf=model_performance_classification_sklearn(rf_estimator_tuned_random,X_test,y_test)
print("Test performance:\n",rf_tuned_test_perf)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.984 0.716636 0.987406 0.830508 0.677819
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator_tuned, X_test, y_test)
%%time
# Choose the type of classifier.
gbc_tuned = GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),random_state=1)
# Grid of parameters to choose from
gbc_param_grid = { "init": [AdaBoostClassifier(random_state=1), DecisionTreeClassifier(random_state=1)],
"n_estimators": np.arange(75,150,25),
"learning_rate": [0.2, 0.05, 1],
"subsample":[0.5,0.7],
"max_features":[0.5,0.7]}
# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = GridSearchCV(gbc_tuned, gbc_param_grid, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train_over, y_train_over)
# Set the clf to the best combination of parameters
gbc_tuned = grid_obj.best_estimator_
# Fit the best algorithm to the data.
gbc_tuned.fit(X_train_over, y_train_over)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Best parameters are {'init': DecisionTreeClassifier(random_state=1), 'learning_rate': 0.2, 'max_features': 0.5, 'n_estimators': 75, 'subsample': 0.5} with CV score=0.9375687323384628:
Wall time: 2h 37min 26s
#Calculating different metrics
gbc_tuned_train_perf=model_performance_classification_sklearn(gbc_tuned,X_train_over,y_train_over)
print("Training performance:\n",gbc_tuned_train_perf)
gbc_tuned_val_perf=model_performance_classification_sklearn(gbc_tuned,X_val,y_val)
print("Validation performance:\n",gbc_tuned_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(gbc_tuned, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 1.0 1.0 1.0 1.0 1.0
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.949083 0.801242 0.516517 0.628119 0.632406
gbc_tuned_test_perf=model_performance_classification_sklearn(gbc_tuned,X_test,y_test)
print("Test performance:\n",gbc_tuned_test_perf)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9476 0.786106 0.51374 0.621387 0.623243
#Creating confusion matrix
confusion_matrix_sklearn(gbc_tuned, X_test, y_test)
%%time
# Choose the type of classifier.
gbc_tuned_random = GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),random_state=1)
# Grid of parameters to choose from
gbc_param_grid_rand = { "init": [AdaBoostClassifier(random_state=1), DecisionTreeClassifier(random_state=1)],
"n_estimators": np.arange(75,150,25),
"learning_rate": [0.2, 0.05, 1],
"subsample":[0.5,0.7],
"max_features":[0.5,0.7]}
# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = RandomizedSearchCV(gbc_tuned_random, gbc_param_grid_rand, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train_over, y_train_over)
# Set the clf to the best combination of parameters
gbc_tuned_random = grid_obj.best_estimator_
# Fit the best algorithm to the data.
gbc_tuned_random.fit(X_train_over, y_train_over)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Best parameters are {'subsample': 0.7, 'n_estimators': 125, 'max_features': 0.7, 'learning_rate': 0.05, 'init': DecisionTreeClassifier(random_state=1)} with CV score=0.9375687323384628:
Wall time: 26min 38s
#Calculating different metrics
gbc_tuned_rnd_train_perf=model_performance_classification_sklearn(gbc_tuned_random,X_train_over,y_train_over)
print("Training performance:\n",gbc_tuned_rnd_train_perf)
gbc_tuned_rnd_val_perf=model_performance_classification_sklearn(gbc_tuned_random,X_val,y_val)
print("Validation performance:\n",gbc_tuned_rnd_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(gbc_tuned_random, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 1.0 1.0 1.0 1.0 1.0
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.949083 0.801242 0.516517 0.628119 0.632406
gbc_tuned_rnd_test_perf=model_performance_classification_sklearn(gbc_tuned_random,X_test,y_test)
print("Test performance:\n",gbc_tuned_rnd_test_perf)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9476 0.786106 0.51374 0.621387 0.623243
#Creating confusion matrix
confusion_matrix_sklearn(gbc_tuned_random, X_test, y_test)
%%time
# Choose the type of classifier.
xgb_tuned = XGBClassifier(random_state=1,eval_metric='logloss')
# Grid of parameters to choose from
xgb_param_grid={'n_estimators':np.arange(150,300,50),
'scale_pos_weight':[5,10],
'learning_rate':[0.1,0.2],
'gamma':[0,3,5],
'subsample':[0.8,0.9]}
# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = GridSearchCV(xgb_tuned, xgb_param_grid,scoring=acc_scorer,cv=5,n_jobs = -1, verbose= 2)
grid_obj = grid_obj.fit(X_train_un, y_train_un)
# Set the clf to the best combination of parameters
xgb_tuned = grid_obj.best_estimator_
# Fit the best algorithm to the data.
xgb_tuned.fit(X_train_un, y_train_un)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Fitting 5 folds for each of 72 candidates, totalling 360 fits
Best parameters are {'gamma': 5, 'learning_rate': 0.2, 'n_estimators': 150, 'scale_pos_weight': 10, 'subsample': 0.9} with CV score=0.8662574260510754:
Wall time: 5min 17s
#Calculating different metrics
xgb_tuned_train_perf=model_performance_classification_sklearn(xgb_tuned,X_train_un,y_train_un)
print("Training performance:\n",xgb_tuned_train_perf)
xgb_tuned_val_perf=model_performance_classification_sklearn(xgb_tuned,X_val,y_val)
print("Validation performance:\n",xgb_tuned_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.993843 1.0 0.987836 0.993881 0.995912
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.894917 0.919255 0.328706 0.484254 0.568068
xgb_tuned_test_perf=model_performance_classification_sklearn(xgb_tuned,X_test,y_test)
print("Test performance:\n",xgb_tuned_test_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned, X_test, y_test)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.8857 0.893967 0.310673 0.461103 0.544098
%%time
# Choose the type of classifier.
xgb_tuned_random = XGBClassifier(random_state=1,eval_metric='logloss')
# Grid of parameters to choose from
xgb_param_grid_random={'n_estimators':np.arange(150,300,50),
'scale_pos_weight':[5,10],
'learning_rate':[0.1,0.2],
'gamma':[0,3,5],
'subsample':[0.8,0.9]}
# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = RandomizedSearchCV(xgb_tuned_random, xgb_param_grid_random,scoring=acc_scorer,cv=5,n_jobs = -1, verbose= 2)
grid_obj = grid_obj.fit(X_train_un, y_train_un)
# Set the clf to the best combination of parameters
xgb_tuned_random = grid_obj.best_estimator_
# Fit the best algorithm to the data.
xgb_tuned_random.fit(X_train_un, y_train_un)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Fitting 5 folds for each of 10 candidates, totalling 50 fits
Best parameters are {'subsample': 0.9, 'scale_pos_weight': 10, 'n_estimators': 200, 'learning_rate': 0.2, 'gamma': 5} with CV score=0.8652393832286936:
Wall time: 50.7 s
#Calculating different metrics
xgb_tuned_rnd_train_perf=model_performance_classification_sklearn(xgb_tuned_random,X_train_un,y_train_un)
print("Training performance:\n",xgb_tuned_rnd_train_perf)
xgb_tuned_rnd_val_perf=model_performance_classification_sklearn(xgb_tuned_random,X_val,y_val)
print("Validation performance:\n",xgb_tuned_rnd_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_random, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.994491 1.0 0.989103 0.994521 0.996341
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.89825 0.919255 0.336173 0.492308 0.574829
xgb_tuned_rnd_test_perf=model_performance_classification_sklearn(xgb_tuned_random,X_test,y_test)
print("Test performance:\n",xgb_tuned_rnd_test_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_random, X_test, y_test)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.8893 0.893967 0.317945 0.469065 0.550671
%time
# Choose the type of classifier.
xgb_tuned_orig = XGBClassifier(random_state=1,eval_metric='logloss')
# Grid of parameters to choose from
xgb_param_grid={'n_estimators':np.arange(150,300,50),
'scale_pos_weight':[5,10],
'learning_rate':[0.1,0.2],
'gamma':[0,3,5],
'subsample':[0.8,0.9]}
# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = GridSearchCV(xgb_tuned_orig, xgb_param_grid,scoring=acc_scorer,cv=5,n_jobs = -1, verbose= 2)
grid_obj = grid_obj.fit(X_train, y_train)
# Set the clf to the best combination of parameters
xgb_tuned_orig = grid_obj.best_estimator_
# Fit the best algorithm to the data.
xgb_tuned_orig.fit(X_train, y_train)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Wall time: 0 ns
Fitting 5 folds for each of 72 candidates, totalling 360 fits
Best parameters are {'gamma': 5, 'learning_rate': 0.1, 'n_estimators': 250, 'scale_pos_weight': 10, 'subsample': 0.8} with CV score=0.8049422330772502:
#Calculating different metrics
xgb_tuned_orig_train_perf=model_performance_classification_sklearn(xgb_tuned_orig,X_train,y_train)
print("Training performance:\n",xgb_tuned_orig_train_perf)
xgb_tuned_orig_val_perf=model_performance_classification_sklearn(xgb_tuned_orig,X_val,y_val)
print("Validation performance:\n",xgb_tuned_orig_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_orig, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.999321 1.0 0.987836 0.993881 0.995912
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9905 0.869565 0.949153 0.907618 0.811083
xgb_tuned_orig_test_perf=model_performance_classification_sklearn(xgb_tuned_orig,X_test,y_test)
print("Test performance:\n",xgb_tuned_orig_test_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_orig, X_test, y_test)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9901 0.864717 0.949799 0.905263 0.805992
%%time
# Choose the type of classifier.
rf_estimator_tuned_over = RandomForestClassifier(random_state=1)
# Grid of parameters to choose from
rf_param_grid = { "n_estimators": [150,250],
"min_samples_leaf": np.arange(1, 3),
"max_features": ['sqrt','log2'],
"max_samples": np.arange(0.2, 0.6, 0.1)
}
# Type of scoring used to compare parameter combinations
acc_scorer = scoring = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = GridSearchCV(rf_estimator_tuned_over, rf_param_grid, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train_over, y_train_over)
# Set the clf to the best combination of parameters
rf_estimator_tuned_over = grid_obj.best_estimator_
# Fit the best algorithm to the data.
rf_estimator_tuned_over.fit(X_train_over, y_train_over)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Best parameters are {'max_features': 'sqrt', 'max_samples': 0.5000000000000001, 'min_samples_leaf': 1, 'n_estimators': 150} with CV score=0.9605285516090613:
Wall time: 50min 40s
#Calculating different metrics
rf_estimator_tuned_over_train_perf=model_performance_classification_sklearn(rf_estimator_tuned_over,X_train_over,y_train_over)
print("Training performance:\n",rf_estimator_tuned_over_train_perf)
rf_estimator_tuned_over_val_perf=model_performance_classification_sklearn(rf_estimator_tuned_over,X_val,y_val)
print("Validation performance:\n",rf_estimator_tuned_over_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator_tuned_over, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.998583 0.997505 0.999659 0.998581 0.995747
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.988667 0.874224 0.911003 0.892235 0.807692
rf_estimator_tuned_over_test_perf=model_performance_classification_sklearn(rf_estimator_tuned_over,X_test,y_test)
print("Test performance:\n",rf_estimator_tuned_over_test_perf)
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator_tuned_over, X_test, y_test)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9893 0.862888 0.936508 0.898192 0.80127
%time
# Choose the type of classifier.
xgb_tuned_over = XGBClassifier(random_state=1,eval_metric='logloss')
# Grid of parameters to choose from
xgb_param_grid={'n_estimators':np.arange(150,300,50),
'scale_pos_weight':[5,10],
'learning_rate':[0.1,0.2],
'gamma':[0,3,5],
'subsample':[0.8,0.9]}
# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(Minimum_Vs_Model_cost, greater_is_better=True)
# Run the grid search
grid_obj = GridSearchCV(xgb_tuned_over, xgb_param_grid,scoring=acc_scorer,cv=5,n_jobs = -1, verbose= 2)
grid_obj = grid_obj.fit(X_train_over, y_train_over)
# Set the clf to the best combination of parameters
xgb_tuned_over = grid_obj.best_estimator_
# Fit the best algorithm to the data.
xgb_tuned_over.fit(X_train_over, y_train_over)
print("Best parameters are {} with CV score={}:" .format(grid_obj.best_params_,grid_obj.best_score_))
Wall time: 0 ns
Fitting 5 folds for each of 72 candidates, totalling 360 fits
Best parameters are {'gamma': 0, 'learning_rate': 0.2, 'n_estimators': 250, 'scale_pos_weight': 10, 'subsample': 0.9} with CV score=0.9887520365943508:
#Calculating different metrics
xgb_tuned_over_train_perf=model_performance_classification_sklearn(xgb_tuned_over,X_train_un,y_train_un)
print("Training performance:\n",xgb_tuned_over_train_perf)
xgb_tuned_over_val_perf=model_performance_classification_sklearn(xgb_tuned_over,X_val,y_val)
print("Validation performance:\n",xgb_tuned_over_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_over, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 1.0 1.0 1.0 1.0 1.0
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.986667 0.899068 0.85905 0.878604 0.821429
xgb_tuned_over_test_perf=model_performance_classification_sklearn(xgb_tuned_over,X_test,y_test)
print("Test performance:\n",xgb_tuned_over_test_perf)
#Creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_over, X_test, y_test)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9841 0.872029 0.842756 0.857143 0.788942
Lets check the important features for model Xgboost with original data
importances = xgb_tuned_orig.feature_importances_
indices = np.argsort(importances)
feature_names = list(X.columns)
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
#Calculating different metrics
rf_estimator_tuned_over_train_perf=model_performance_classification_sklearn(rf_estimator_tuned_over,X_train_over,y_train_over)
print("Training performance:\n",rf_estimator_tuned_over_train_perf)
rf_estimator_tuned_over_val_perf=model_performance_classification_sklearn(rf_estimator_tuned_over,X_val,y_val)
print("Validation performance:\n",rf_estimator_tuned_over_val_perf)
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator_tuned_over, X_val, y_val)
Training performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.998583 0.997505 0.999659 0.998581 0.995747
Validation performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.988667 0.874224 0.911003 0.892235 0.807692
rf_estimator_tuned_over_test_perf=model_performance_classification_sklearn(rf_estimator_tuned_over,X_test,y_test)
print("Test performance:\n",rf_estimator_tuned_over_test_perf)
#Creating confusion matrix
confusion_matrix_sklearn(rf_estimator_tuned_over, X_test, y_test)
Test performance:
Accuracy Recall Precision F1 Minimum_Vs_Model_cost
0 0.9893 0.862888 0.936508 0.898192 0.80127
importances = rf_estimator_tuned_over.feature_importances_
indices = np.argsort(importances)
feature_names = list(X.columns)
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
numeric_transformer = Pipeline(steps=[("imputer", SimpleImputer(strategy="median"))])
# Creating new pipeline with best parameters
model = Pipeline(
steps=[
("pre", numeric_transformer),
(
"RF_OverSampled",
RandomForestClassifier(random_state=1,
max_features='sqrt',
max_samples=0.5000000000000001,
min_samples_leaf=1,
n_estimators=150,
),
),
]
)
# Fit the model on training data
model.fit(X_train_over, y_train_over)
Pipeline(steps=[('pre',
Pipeline(steps=[('imputer',
SimpleImputer(strategy='median'))])),
('RF_OverSampled',
RandomForestClassifier(max_features='sqrt',
max_samples=0.5000000000000001,
n_estimators=150, random_state=1))])